Streaming media data transmission method and system, and electronic device and storage medium

Abstract

Disclosed in the present application are a streaming media data transmission method and system, and an electronic device and a storage medium. The method is applied to a server which runs a protocol stack, and the server is used for performing data transmission with a playback client by means of a network, and executing the following method by means of the protocol stack: in response to a target request from a playback client, acquiring an attribute information set of the playback client from the target request, wherein the target request is used for requesting the transmission of streaming media data in a playback service scenario, and the attribute information set comprises at least one piece of attribute information of the playback client during operation, and / or at least one piece of attribute information of a terminal device, on which the playback client is installed, during operation; determining from a transmission policy set a target transmission policy which matches the attribute information set and a network feature of the network; and transmitting the streaming media data to the playback client according to the target transmission policy.

Description

Methods, systems, electronic devices and storage media for streaming media data transmission

[0001] Cross-reference

[0002] This application claims priority to Chinese Patent Application No. 202510128523.2, filed on January 27, 2025, entitled “Method, System, Electronic Device and Storage Medium for Transmitting Streaming Media Data”, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application relates to the field of network transmission technology, and more specifically, to a method, system, electronic device, and storage medium for transmitting streaming media data. Background Technology

[0004] Currently, kernel congestion control technology plays a crucial role in streaming media transmission, especially live broadcasting, directly impacting the transmission quality of video and audio data over the network. However, current kernel congestion control solutions have significant limitations, primarily in their insufficient ability to perceive the characteristics of client devices and network conditions.

[0005] The aforementioned technical solutions are often based on common congestion control algorithms, such as Compound TCP with Incremental Unknown Linear Congestion Increase Avoidance (CUBIC) and Bottleneck Bandwidth and Round-trip time Congestion Control (BBR) from the Transmission Control Protocol (TCP).

[0006] However, the aforementioned mechanisms did not fully consider the dynamic characteristics of terminal device hardware capabilities, operating system types, or network connections during their initial design. They exhibit significant shortcomings in client device awareness and network condition adaptability, directly leading to insufficient service quality for end users and becoming a critical issue that urgently needs to be addressed in the current streaming media transmission field. Therefore, the technical problem of low transmission quality for streaming media data still exists.

[0007] There is currently no effective solution to the above problems. Summary of the Invention

[0008] This application provides a method, system, electronic device, and storage medium for transmitting streaming media data, in order to at least solve the technical problem of poor transmission quality of streaming media data.

[0009] According to one aspect of the embodiments of this application, a method for transmitting streaming media data is provided. This method can be applied to a server running a protocol stack, the server being used to transmit data with a playback client via a network, and executing the following method through the protocol stack: in response to a target request from the playback client, obtaining an attribute information set of the playback client from the target request, wherein the target request is for requesting the transmission of streaming media data in a playback service scenario, the attribute information set including at least one attribute information of the playback client during operation, and / or at least one attribute information of the terminal device on which the playback client is installed during operation; determining a target transmission strategy in a transmission strategy set that matches the attribute information set and network characteristics of the network, wherein the transmission strategy in the transmission strategy set is used to represent the rules for data transmission by the server in a playback service scenario, and the network characteristics are used to represent the attributes of the network during operation; transmitting the streaming media data to the playback client according to the target transmission strategy, wherein the streaming media data is used for playback on the playback client.

[0010] According to another aspect of the embodiments of this application, another method for transmitting streaming media data is provided. This method is applied to a content delivery network node running a live streaming protocol stack, the content delivery network node being used to transmit data with a playback client via a network, and performing the following method through the live streaming protocol stack: In response to a target request from the playback client, parsing an attribute information set of the playback client from the target request, wherein the target request is for requesting the transmission of streaming media data in a live streaming service scenario, the attribute information set including at least one attribute information of the playback client during operation, and / or at least one attribute information of the terminal device on which the playback client is installed during operation; determining a target transmission strategy in a transmission strategy set that matches the attribute information set and network characteristics of the network, wherein the transmission strategy in the transmission strategy set is used to represent the rules for data transmission by the content delivery network node in a live streaming service scenario, and the network characteristics are used to represent the attributes of the network during operation; transmitting the streaming media data to the playback client according to the target transmission strategy, wherein the streaming media data is used for playback on the playback client.

[0011] According to another aspect of the embodiments of this application, another method for transmitting streaming media data is provided. The method is applied to a server running a protocol stack, the server being used to transmit data with a playback client over a network, and executing the following method through the protocol stack: obtaining a target request from the playback client by calling a first interface, wherein the first interface includes a first parameter, the parameter value of the first parameter being the target request; in response to the target request, obtaining an attribute information set of the playback client from the target request, wherein the target request is used to request the transmission of streaming media data in a playback service scenario, the attribute information set including at least one attribute information of the playback client during operation, and / or at least one attribute information of the terminal device on which the playback client is installed during operation; determining a target transmission strategy in a transmission strategy set that matches the attribute information set and network characteristics of the network, wherein the transmission strategy in the transmission strategy set is used to represent the rules for data transmission by the server in a playback service scenario, and the network characteristics are used to represent the attributes of the network during operation; transmitting the streaming media data to the playback client according to the target transmission strategy by calling a second interface, wherein the second interface includes a second parameter, the parameter value of the second parameter being the target transmission strategy, and the streaming media data being used for playback on the playback client.

[0012] According to another aspect of the embodiments of this application, a streaming media data transmission system is provided. The system may include: a server running a protocol stack and a playback client; the server is used to transmit data with the playback client via a network; the playback client is used to send a target request, wherein the target request is used to request the transmission of streaming media data in a playback service scenario; the server is used to respond to the target request via the protocol stack, obtaining an attribute information set of the playback client from the target request, wherein the attribute information set includes at least one attribute information of the playback client during operation, and / or at least one attribute information of the terminal device on which the playback client is installed during operation; in a transmission strategy set, determining a target transmission strategy that matches the attribute information set and network characteristics of the network, wherein the transmission strategy in the transmission strategy set represents the rules for the server to transmit data in a playback service scenario, and the network characteristics represent the attributes of the network during operation; and transmitting the streaming media data to the playback client according to the target transmission strategy; wherein the playback client is used to play the streaming media data.

[0013] According to another aspect of the embodiments of this application, a streaming media data transmission apparatus is provided. The apparatus may include: a first acquisition component, configured to, in response to a target request from a playback client, acquire an attribute information set of the playback client from the target request, wherein the target request is for requesting the transmission of streaming media data in a playback service scenario, and the attribute information set includes at least one attribute information of the playback client during operation, and / or at least one attribute information of the terminal device on which the playback client is installed during operation; a first determination component, configured to, in a transmission strategy set, determine a target transmission strategy that matches the attribute information set and network characteristics of the network, wherein the transmission strategy in the transmission strategy set represents the rules for data transmission by the server in a playback service scenario, and the network characteristics represent the attributes of the network during operation; and a first transmission component, configured to transmit the streaming media data to the playback client according to the target transmission strategy, wherein the streaming media data is used for playback on the playback client.

[0014] According to another aspect of the embodiments of this application, another streaming media data transmission apparatus is provided. The apparatus may include: a parsing component, configured to, in response to a target request from a playback client, parse an attribute information set of the playback client from the target request, wherein the target request is for requesting the transmission of streaming media data in a live streaming service scenario, and the attribute information set includes at least one attribute information of the playback client during operation, and / or at least one attribute information of the terminal device on which the playback client is installed during operation; a second determining component, configured to, in a transmission strategy set, determine a target transmission strategy that matches the attribute information set and network characteristics of the network, wherein the transmission strategies in the transmission strategy set represent the rules for data transmission by content distribution network nodes in a live streaming service scenario, and the network characteristics represent the attributes of the network during operation; and a second transmission component, configured to transmit the streaming media data to the playback client according to the target transmission strategy, wherein the streaming media data is used for playback on the playback client.

[0015] According to another aspect of the embodiments of this application, another streaming media data transmission apparatus is provided. The apparatus may include: a first invocation component, configured to obtain a target request from a playback client by invoking a first interface, wherein the first interface includes a first parameter, the parameter value of which is the target request; a second acquisition component, configured to, in response to the target request, obtain an attribute information set of the playback client from the target request, wherein the target request is for requesting the transmission of streaming media data in a playback service scenario, the attribute information set including at least one attribute information of the playback client during operation, and / or at least one attribute information of the terminal device on which the playback client is installed during operation; a third determination component, configured to determine a target transmission strategy that matches the attribute information set and network characteristics of the network in a transmission strategy set, wherein the transmission strategy in the transmission strategy set represents the rules for data transmission by the server in a playback service scenario, and the network characteristics represent the attributes of the network during operation; and a second invocation component, configured to transmit the streaming media data to the playback client according to the target transmission strategy by invoking a second interface, wherein the second interface includes a second parameter, the parameter value of which is the target transmission strategy, and the streaming media data is used for playback on the playback client.

[0016] According to another aspect of the embodiments of this application, an electronic device is also provided. The electronic device may include a memory and a processor: the memory stores computer-executable instructions, and the processor executes the computer-executable instructions, wherein when the processor executes the computer-executable instructions, it implements the streaming media data transmission method of the embodiments of this application.

[0017] According to another aspect of the embodiments of this application, a processor is also provided. The processor is used to run a program, wherein the streaming media data transmission method of the embodiments of this application is executed during program execution.

[0018] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided. This computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device where the storage medium is located to perform the streaming media data transmission method described in the embodiments of this application.

[0019] According to another aspect of the embodiments of this application, a computer program product is also provided. This computer program product includes a computer program that, when executed by a processor, implements the streaming media data transmission method described in the embodiments of this application.

[0020] In this embodiment, if streaming media data needs to be transmitted, the target request of the playback client can be monitored. If a target request for streaming media data in a scenario requiring playback service is detected on the playback client, the attribute information set of the playback client can be obtained from the target request. A target transmission strategy matching the attribute information set and network characteristics can be determined from the transmission strategy set. The streaming media data can then be transmitted to the playback client for playback according to the target transmission strategy. In this embodiment, by monitoring the target request of the terminal device, obtaining terminal attribute information, and combining network characteristics for dynamic strategy decision-making and self-adjustment, a comprehensive adjustment of the streaming media data service quality is ultimately achieved, effectively improving the transmission quality of streaming media data. Through the above-mentioned end-edge fusion strategy in highly interactive task scenarios, the user experience can be significantly improved and the service competitiveness enhanced, thereby achieving the technical effect of improving the transmission quality of streaming media data and solving the technical problem of poor streaming media data transmission quality.

[0021] It is worth noting that the general description above and the detailed description that follow are merely for illustrative purposes and do not constitute a limitation on this application. Attached Figure Description

[0022] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments of this application and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0023] Figure 1 is a schematic diagram of an application scenario of a streaming media data transmission method according to an embodiment of this application;

[0024] Figure 2 is a flowchart of a streaming media data transmission method according to an embodiment of this application;

[0025] Figure 3 is a flowchart of another method for transmitting streaming media data according to an embodiment of this application;

[0026] Figure 4 is a flowchart of another method for transmitting streaming media data according to an embodiment of this application;

[0027] Figure 5 is a schematic diagram of a streaming media data transmission system according to an embodiment of this application;

[0028] Figure 6 is a schematic diagram of a content delivery network protocol stack evolution architecture according to an embodiment of this application;

[0029] Figure 7 is a schematic diagram of the key points of the evolution of a content delivery network protocol stack according to an embodiment of this application;

[0030] Figure 8 is a schematic diagram of a first embodiment of a data-based live streaming protocol stack architecture according to an embodiment of this application;

[0031] Figure 9 is a schematic diagram of a second embodiment of a four-seven combined live streaming protocol stack architecture according to an embodiment of this application;

[0032] Figure 10 is a schematic diagram of a third embodiment of a live streaming protocol stack architecture according to an embodiment of this application;

[0033] Figure 11 is a schematic diagram showing the design details of a third embodiment of a live streaming protocol stack architecture according to an embodiment of this application;

[0034] Figure 12 is a schematic diagram of congestion control initialization under operating system and network conditions according to an embodiment of this application;

[0035] Figure 13 is a schematic diagram of a network quality-based optimization decision according to an embodiment of this application;

[0036] Figure 14 is a schematic diagram of a runtime strategy self-optimization and degradation mechanism according to an embodiment of this application;

[0037] Figure 15 is a schematic diagram of a streaming media data transmission device according to an embodiment of this application;

[0038] Figure 16 is a schematic diagram of another streaming media data transmission device according to an embodiment of this application;

[0039] Figure 17 is a schematic diagram of another streaming media data transmission device according to an embodiment of this application;

[0040] Figure 18 is a structural block diagram of a computer terminal according to an embodiment of this application;

[0041] Figure 19 is a block diagram of an electronic device for a streaming media data transmission method according to an embodiment of this application;

[0042] Figure 20 is a hardware structure block diagram of a computer terminal (or mobile device) for implementing a streaming media data transmission method according to an embodiment of this application;

[0043] Figure 21 is a structural block diagram of a computing environment for a streaming media data transmission method according to an embodiment of this application. Detailed Implementation

[0044] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present application.

[0045] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or components is not necessarily limited to those explicitly listed, but may include other steps or components not explicitly listed or inherent to the aforementioned process, method, product, or device.

[0046] First, some nouns or terms that appear in the description of the embodiments of this application shall be interpreted as follows:

[0047] Differentiated protocol stacks refer to protocol stacks that provide different transmission efficiencies and qualities for different network environments and terminal devices, in order to meet the streaming media transmission needs in different scenarios.

[0048] Service quality refers to the overall performance of transmission efficiency and quality during streaming media transmission, including transmission speed, smoothness, and buffering rate.

[0049] Buffering refers to delays, pauses, and stuttering during streaming media transmission, which is a sign of poor service quality.

[0050] Low bitrate refers to the use of less data compression during streaming media transmission, resulting in lower transmission efficiency but reducing network bandwidth consumption.

[0051] High bitrate refers to the use of greater data compression during streaming media transmission, resulting in higher transmission efficiency, but also increasing network bandwidth consumption.

[0052] Link buffer utilization refers to the utilization rate of the buffer in a network link, that is, the proportion of data stored in the buffer to the maximum capacity of the network.

[0053] Stream type refers to the type of streaming media data during streaming media transmission, including video, audio, and streaming media.

[0054] Minimum Round Trip Time (Min RTT) refers to the shortest round-trip time in network communication between the sending of a data packet and the receipt of a response to that data packet. It is an important indicator for measuring network latency.

[0055] According to an embodiment of this application, a method for transmitting streaming media data is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0056] The streaming media data transmission method provided in this application embodiment can be applied to the application scenario shown in Figure 1, but is not limited thereto. In the application scenario shown in Figure 1, the server 10 can be in the cloud. The server 10 can connect to one or more playback clients (also referred to as clients) 20 through a local area network (LAN), a wide area network (WAN), an internet connection, or other types of data networks. The playback clients 20 here can include, but are not limited to, smartphones, tablets, laptops, PDAs, personal computers, smart home devices, in-vehicle devices, etc. The client devices can also be referred to as terminal devices and client devices. The client devices together constitute the client relative to the server. The client devices can be playback clients. An operation interface for user operation can be deployed on the graphical user interface of the client device, which can be a cloud desktop. The client device 20 can interact with the user through the graphical user interface to realize the streaming media data transmission method provided in this application embodiment.

[0057] In this embodiment, the system comprising the playback client 20 and the server 10 can perform the following steps: If it is necessary to play streaming media data in a certain playback service scenario, the user can perform corresponding operations on the operation interface of the playback client 20 and input a target request corresponding to the streaming media data to be played. The target request can be transmitted to the server 10 via the network. Specifically, the following steps can be performed in the server 10.

[0058] Step S102: In response to a target request from the playback client, obtain the attribute information set of the playback client from the target request; Step S104: In the transmission strategy set, determine a target transmission strategy that matches the attribute information set and the network characteristics of the network; Step S106: Transmit the streaming media data to the playback client according to the target transmission strategy.

[0059] During the above process, the streaming media data obtained from server 10 that meets the aforementioned target request can be sent to playback client 20 via the network. On playback client 20, the acquired streaming media data can be loaded and played.

[0060] In this embodiment, by monitoring the target requests of terminal devices in real time and obtaining terminal attribute information, dynamic strategy decisions and self-adjustments are made in conjunction with network characteristics, ultimately achieving comprehensive optimization of streaming media data service quality and effectively improving the transmission quality of streaming media data. Through the aforementioned end-edge fusion strategy in real-time, highly interactive task scenarios, user experience can be significantly improved and service competitiveness enhanced, thereby achieving the technical effect of improving the transmission quality of streaming media data and solving the technical problem of poor streaming media data transmission quality.

[0061] This application proposes the following method. In the above application scenario, this application provides a streaming media data transmission method from the server side, as shown in Figure 2. Figure 2 is a flowchart of a streaming media data transmission method according to an embodiment of this application. As shown in Figure 2, it is applied to a server running a protocol stack. The server is used to transmit data with the playback client over the network and executes the following methods through the protocol stack.

[0062] Step S202: In response to the target request from the playback client, obtain the attribute information set of the playback client from the target request.

[0063] In the technical solution provided in step S202 of this application, the server can be a node of the entire Content Delivery Network (CDN), also known as a CDN server. The CDN server possesses high-performance data processing and transmission capabilities, enabling optimized processing for a large number of client requests, ensuring efficient data transmission and a smooth user viewing experience. The playback client can be a client terminal player. The target request can be used to request the transmission of streaming media data in a playback service scenario. The playback service scenario can be a live streaming media task scenario. The target request is sent by the playback client (i.e., the client terminal player) to request the transmission of streaming media data, especially live streaming media data in a playback service scenario. This means that when a user needs to watch a certain live content, they can send a target request to the CDN server. The content of the target request includes the streaming media data information to be obtained and certain attribute information of the playback client itself.

[0064] Optionally, the attribute information set includes at least one attribute of the playback client during operation, and / or at least one attribute of the terminal device on which the playback client is installed during operation. The attribute information in the attribute information set may include terminal information, client terminal information, or various types of information on the terminal side. The playback client's operational information may include player cache size, current playback status (e.g., paused, buffering, playing), etc., reflecting the player's data processing capabilities and status. Terminal device information may include device type (e.g., mobile phone), hardware configuration (e.g., memory), operating system type, network connection type and quality (e.g., bandwidth, packet loss rate), etc., providing the CDN server with insights into terminal device performance and network conditions. The above attribute information is for illustrative purposes only and is not specifically limited here.

[0065] In this embodiment, if the server detects a target request from the playback client, it can obtain the attribute information set of the playback client from the target request.

[0066] Optionally, this embodiment illustrates how the CDN server responds to a playback client's request and extracts the playback client's attribute information set from the request.

[0067] Optionally, the playback client (i.e., the streaming media player on the user's device) can send a target request to the CDN server. The purpose of this target request is to obtain the required streaming media data (such as video or audio files) for real-time playback on the playback client. The target request contains information about the playback client's demand for streaming media data, as well as the playback client's attribute information.

[0068] Optionally, when the CDN server receives a target request, it can respond to the target request. Responding to the target request is a response mechanism that includes steps such as parsing and processing the target request, and preparing for subsequent data transmission.

[0069] Optionally, after responding to the target request, the CDN server can extract the attribute information set of the playback client from the target request. The attribute information set can be multi-dimensional, covering the state information of the playback client during operation and the characteristic information of the terminal device itself.

[0070] Optionally, parsing the target request and accurately extracting the attribute information set requires the CDN server to possess a high degree of intelligence and flexibility. It can parse the request header information of the target request, which contains detailed attributes of the terminal device and playback client. For example, the request header may contain a User-Agent field, which provides information about the type and version of the client software and hardware; it may also contain custom headers containing dynamic information such as the current player cache status and network quality awareness.

[0071] Optionally, the acquired attribute information set is crucial for subsequent transmission strategy decisions. This attribute information not only allows the CDN server to determine the specific needs and capabilities of each playback client, but also to identify the performance metrics of the terminal device and its network environment. Based on this attribute information, the CDN server can make more precise strategy choices. For example, for high-performance devices and excellent network environments, the CDN server can choose to adopt strategies such as high bitrate transmission, a larger initial congestion window, and faster pacing control to maximize transmission efficiency and user experience. For low-performance devices or poor network conditions, the server can adopt more conservative strategies, such as low bitrate transmission, a smaller congestion window, and slower pacing control, to ensure stable data transmission and avoid stuttering and latency when playing streaming media.

[0072] In this embodiment of the application, by responding to the target request and obtaining the attribute information set of the playback client, the CDN server can make intelligent and differentiated strategy decisions to provide service quality that meets user requirements for streaming media transmission in different scenarios.

[0073] Step S204: In the transmission strategy set, determine the target transmission strategy that matches the attribute information set and the network characteristics of the network.

[0074] In the technical solution provided in step S204 of this application, the transmission strategies in the transmission strategy set can be used to represent the rules for data transmission by the server in a playback service scenario. The transmission strategy set refers to a set of rules or algorithms preset by the CDN server to optimize data transmission. These transmission strategies cover various methods of congestion control, including but not limited to window size adjustment, pacing, slow start characteristics, and packet loss recovery mechanisms, aiming to address the challenges of different network conditions and playback client attributes. The transmission strategy set is the core of the CDN server's implementation of intelligent data transmission, achieving efficient and stable streaming media transmission by dynamically selecting the most suitable strategy. Network characteristics can be used to represent the attributes of the network during operation. Network characteristics refer to the set of attributes of the network's operating state at a certain moment, including but not limited to network bandwidth, packet loss rate, round-trip time (RTT), link utilization, and network congestion level. These network characteristics are key indicators for measuring network quality and transmission conditions, and are crucial for determining transmission strategies. Network characteristics can change over time; therefore, the CDN server needs to monitor these indicators in real time or periodically to ensure that the transmission strategy accurately reflects the current network condition. The target transmission strategy can be a transmission strategy that meets the requirements of the transmission strategy set, such as a congestion control strategy that meets the requirements.

[0075] In this embodiment, after obtaining the attribute information set of the playback client from the target request, a target transmission strategy that matches the attribute information set and the network characteristics of the network can be determined from the transmission strategy set.

[0076] Optionally, a suitable target transmission strategy can be selected from a preset transmission strategy set based on the attribute information set of the playback client and the characteristics of the current network.

[0077] Optionally, the transmission strategy set includes a variety of different transmission strategies, which are pre-designed based on different network conditions and terminal performance, aiming to adapt to various possible transmission environments. Each transmission strategy defines specific parameters and rules, such as congestion window size, pacing rate, slow start behavior, retransmission mechanism, etc., to ensure improved data transmission efficiency, stability, and user experience.

[0078] Optionally, after receiving the target request and analyzing the attribute information set and network characteristics, a transmission strategy that meets the requirements can be selected from the transmission strategy set as the target transmission strategy.

[0079] For example, by taking the playback client's attribute information and real-time network characteristics as input, a pre-trained or designed algorithm (e.g., decision tree, neural network model, etc.) is used to match the most suitable transmission strategy. This algorithm can evaluate which transmission strategy can provide suitable transmission performance and user experience under current conditions. The process and method described above for determining the target transmission strategy from the set of transmission strategies are merely illustrative and are not specifically limited here. Any method that can select a transmission strategy that meets the current conditions for transmitting streaming media data based on its own decision-making ability is within the protection scope of the embodiments of this application, and will not be described in detail here.

[0080] Optionally, each transmission strategy has its specific applicable scenarios, and the expected performance of each strategy can be evaluated based on the current attribute information and network characteristics. For example, for high-performance devices and high-quality network environments, a strategy with a high bit rate, a large congestion window, and a fast pacing rate can be selected; while for low-performance devices or unstable networks, a strategy with a low bit rate, a small congestion window, and a slow pacing rate is preferred to avoid excessive consumption of network resources or causing terminal resource bottlenecks.

[0081] Optionally, after determining the target transmission strategy, the transmission strategy parameters can be dynamically adjusted based on real-time feedback (such as packet loss, latency changes, etc.) to cope with fluctuations in network conditions. This adaptability (self-decision-making capability) ensures that the transmission strategy can be continuously optimized, maintaining good transmission quality even when network conditions change.

[0082] Optionally, the final selected target transmission strategy will be applied to the current TCP connection, guiding operations such as the sending rate of data packets (e.g., streaming media data), retransmission mechanisms, and window size adjustments. By implementing the above target transmission strategy, network resources can be maximized, packet loss and latency can be reduced, and efficient and stable transmission of streaming media data can be ensured, thereby improving playback quality and reducing stuttering and first-frame latency.

[0083] In this embodiment, the method described above intelligently selects the most suitable strategy from a set of transmission strategies to optimize streaming media transmission by utilizing the attribute information of the playback client and real-time network characteristics. This process demonstrates the intelligence and flexibility of transmitting streaming media data, ensuring a suitable playback experience for users in a constantly changing network environment. By dynamically adjusting the transmission strategy, network congestion can be effectively addressed, and transmission efficiency can be improved.

[0084] Step S206: Transmit streaming media data to the playback client according to the target transmission strategy.

[0085] In the technical solution provided by step S206 of this application, streaming media data can be used for playback on a playback client.

[0086] In this embodiment, after determining the target transmission strategy that matches the attribute information set and network characteristics from the transmission strategy set, the streaming media data can be transmitted to the playback client for playback according to the target transmission strategy.

[0087] Optionally, after determining the target transmission strategy, the behavior of TCP connections or data transmission will be adjusted according to that strategy. For example, this can be done by adjusting the congestion window size, setting the pacing rate, controlling the aggressiveness of slow start, and adjusting the retransmission mechanism. Implementing the target transmission strategy means translating the selected strategy into actual data transmission parameters to guide subsequent data sending behavior.

[0088] Optionally, once the target transmission strategy is applied, streaming media data is transmitted to the playback client. Streaming media data can be sent over the network in the form of packets or data packets. During the aforementioned stages, the rate, order, and window size of streaming media data transmission are all constrained by the target transmission strategy to ensure the efficiency, stability, and playback quality of streaming media data transmission.

[0089] Optionally, after receiving the streaming media data, the playback client unpacks it and sends it to the playback buffer, then decodes and plays it according to the buffer's state and the order of the data packets. Since the transmission strategy has been optimized for the specific attributes of the playback client and the network environment, the process of receiving and playing streaming media data should be smoother, reducing stuttering and latency, and improving playback fluency and first-frame playback speed.

[0090] Optionally, data transmission is executed according to the target strategy, but in actual operation, a certain degree of dynamic adjustment capability can also be provided. This means that network characteristics and changes in the state of the playback client can be continuously monitored, and the transmission strategy can be adjusted as necessary to cope with fluctuations in the network environment or updates to the playback client's attribute information. The above-mentioned adaptive adjustment is achieved through real-time feedback and a strategy state machine, ensuring that data transmission can always be in a state that meets the requirements.

[0091] Optionally, by executing streaming media data transmission according to the target transmission strategy, personalized transmission services can be provided for specific playback clients and network environments, effectively reducing stuttering and first-frame latency, and improving playback quality. This process of optimizing user experience is the core objective of the congestion control algorithm and a direct result of intelligently matching transmission strategies and data transmission behaviors.

[0092] In this embodiment, based on a defined target transmission strategy, streaming media data is efficiently and stably transmitted to the playback client, ensuring smooth and timely playback. This process not only demonstrates intelligent matching capabilities but also includes dynamic adjustment and feedback mechanisms, ensuring that the playback experience remains at a high level even when network conditions change. By optimizing the data transmission strategy, service quality can be significantly improved, meeting user needs in different scenarios.

[0093] Through steps S202 to S206 of this application, if streaming media data needs to be transmitted, the target request of the playback client can be monitored in real time. If a target request for obtaining streaming media data in a playback service scenario is detected on the playback client, the attribute information set of the playback client can be obtained from the target request. A target transmission strategy matching the attribute information set and network characteristics can be determined from the transmission strategy set. The streaming media data can be transmitted to the playback client for playback according to the target transmission strategy. In this embodiment, the server obtains terminal attribute information by monitoring the target request of the terminal device in real time, and performs dynamic strategy decision-making and self-adjustment in combination with network characteristics, ultimately achieving comprehensive optimization of the streaming media data service quality and effectively improving the transmission quality of streaming media data. Through the above-mentioned end-edge fusion strategy in real-time highly interactive task scenarios, the user experience can be significantly improved and the service competitiveness enhanced, thereby achieving the technical effect of improving the transmission quality of streaming media data and solving the technical problem of poor transmission quality of streaming media data.

[0094] The method described in this embodiment will be further described below.

[0095] As an optional implementation, the quality index achieved by transmitting streaming media data according to the target transmission strategy is higher than the quality index achieved by transmitting streaming media data according to transmission strategies other than the target transmission strategy in the transmission strategy set.

[0096] In this embodiment, the quality index achieved by transmitting streaming media data according to the target transmission strategy is higher than the quality index achieved by transmitting streaming media data according to a transmission strategy other than the target transmission strategy. Here, the quality index can be a service quality evaluation index.

[0097] Optionally, this embodiment describes the significant effect of implementing the target transmission strategy, that is, when transmitting streaming media data according to the target transmission strategy, the service quality indicators (such as first frame playback latency, stuttering rate, video smoothness, etc.) achieved are higher than those achieved when using any other transmission strategy in the transmission strategy set. The key point of the above implementation is the efficiency and accuracy of the target transmission strategy.

[0098] Optionally, the target transmission strategy is carefully selected through prior attribute information set and network characteristic analysis, aiming to provide a data transmission configuration that meets the requirements for a specific playback client and the current network environment. This means that when transmission is executed according to the target strategy, challenges such as network congestion and link instability can be overcome, the available bandwidth can be utilized to the maximum extent, and latency and packet loss during data transmission can be reduced, thereby ensuring that streaming media data can reach the playback client faster and more stably.

[0099] Optionally, to verify the superiority of the target transmission strategy, the quality metrics achieved when transmitting streaming media data according to the target strategy and other strategies in the transmission strategy set besides the target strategy can be compared. The above comparison is based on actual transmission results and user experience data, including but not limited to key performance indicators such as first-frame playback latency, number of stutters, video smoothness, and audio synchronization.

[0100] Optionally, during transmission according to the target strategy, real-time changes in network characteristics and the reception status of the playback client can be continuously monitored. The transmission strategy can be continuously adjusted through a feedback mechanism to ensure its adaptability and continuous optimization of transmission quality. This real-time monitoring and dynamic adjustment capability is crucial to ensuring that the target strategy consistently demonstrates appropriate effectiveness.

[0101] Optionally, to demonstrate that a target transmission strategy is superior to other transmission strategies, it is typically necessary to evaluate it using specific quantitative metrics. This can include collecting extensive transmission data and user feedback from actual commercial deployments. Data analysis can be used to compare changes in transmission efficiency, latency, and buffering rates under different strategies, thereby quantifying the optimization effect of the target strategy. In the long run, implementing a target transmission strategy not only instantly improves the quality of streaming media playback but also reduces unnecessary data retransmissions and lowers network bandwidth consumption, resulting in significant cost savings. Furthermore, by enhancing the user experience, it can also increase user satisfaction and loyalty.

[0102] In this embodiment, streaming media data transmission is performed according to the target transmission strategy, which not only demonstrates the intelligence and efficiency of the algorithm in matching playback client attributes and network characteristics, but also proves its significant advantages in improving user experience, optimizing transmission efficiency and reducing costs through actual service quality indicators.

[0103] As an optional implementation, step S204 involves determining a target transmission strategy that matches the attribute information set and network characteristics within the transmission strategy set, including: determining a strategy level that matches the attribute information set and network characteristics, wherein the strategy level is used to represent the stringency required by the server for the data transmission process in a playback service scenario; and determining a target transmission strategy that meets the strategy level within the transmission strategy set.

[0104] In this embodiment, during the process of determining the target transmission strategy that matches the attribute information set and network characteristics within the transmission strategy set, a strategy level matching the attribute information set and network characteristics can be determined. From the transmission strategy set, a target transmission strategy that meets the strategy level is determined. The strategy level can represent the stringency required by the server for the data transmission process in a playback service scenario, and may include high-level strategy, moderate-level strategy, and weak-network strategy, etc. The number and specific levels of the above-mentioned strategy levels are merely illustrative and are not specifically limited here.

[0105] Optionally, this embodiment is a key step in the congestion control algorithm for intelligent decision-making of transmission strategies. Its core lies in determining a strategy level by analyzing the attribute information set of the playback client and the real-time characteristics of the network, and then selecting the target transmission strategy in the transmission strategy set that best matches the above strategy level.

[0106] Optionally, the policy level is set based on a comprehensive evaluation of the playback client's attribute information set and network characteristics. The aforementioned policy level reflects the server's expected requirements for transmission quality and efficiency during data transmission. It is determined based on client device performance (e.g., hardware level, operating system, player cache state), network conditions (e.g., bandwidth, latency, packet loss rate), and task requirements (e.g., video quality preferences, real-time requirements, etc.). Policy levels typically include high-intensity policy level, moderate policy level, and weak network policy level.

[0107] Optionally, the high-aggression strategy level can be used in scenarios where client devices have high performance, good network conditions, and the task requirements are sensitive to transmission efficiency. The server will tend to adopt a more aggressive transmission strategy, such as a larger congestion window, a faster pacing rate, and a shorter slow start time, to fully utilize network resources and improve transmission speed. The moderate strategy level is suitable for transmission under normal or moderate network conditions. The strategy is relatively balanced, ensuring both transmission stability and data transmission efficiency, making it suitable as the default strategy in most scenarios. The weak network strategy level can be used in environments where client device performance is limited or network conditions are poor. The server will adopt a more conservative strategy, such as a smaller congestion window, a slower pacing rate, and a longer slow start time, to avoid network congestion, reduce packet retransmissions, and ensure that basic streaming media playback quality is maintained even in unfavorable network environments.

[0108] Optionally, after determining the policy level, target transmission policies that meet the requirements of that policy level can be selected from the transmission policy set. The above steps rely on preset rules or algorithms to quickly locate suitable policies based on the policy level. Determining the target transmission policy ensures that data transmission behavior matches the current network environment and user needs, providing a satisfactory playback experience and quality of service.

[0109] Optionally, the determination of the policy level and target transmission policy is a dynamic process that can be adjusted as network characteristics change in real time and client attribute information is updated. For example, if network conditions suddenly deteriorate, the policy level can be automatically downgraded from a high-risk policy level to a moderate or weak-network policy level, and the transmission policy can be adjusted accordingly to prevent excessive consumption of network resources and resulting in a decline in service quality. By intelligently determining a policy level that matches client attributes and network characteristics, and then selecting a target transmission policy from it, the quality and efficiency of streaming media playback can be significantly improved, reducing stuttering, latency, and other unpleasant experiences. At the same time, it can effectively control the consumption of network resources, reduce retransmissions and bandwidth waste, thereby improving user experience while reducing operating costs and enhancing the overall value of the task.

[0110] In this embodiment, by dynamically determining the policy level and selecting the corresponding target transmission policy, fine-grained control and optimization of the streaming media data transmission process are achieved, ensuring that high-quality playback services can be provided under various network conditions. This is an important part of the entire congestion control algorithm to improve service quality.

[0111] As an optional implementation, the method further includes: determining the playback stage of the streaming media data playback by the playback client; determining a strategy level that matches the attribute information set and network characteristics, including: determining the attribute information set and network characteristics that match the playback stage, wherein different playback stages correspond to different attribute information sets.

[0112] In this embodiment, the playback stage of the streaming media data played by the playback client can be determined. In determining the policy level that matches the attribute information set and network characteristics, a policy level matching the attribute information set under the determined stage and the network characteristics under the playback stage can be determined. The playback stage can include the first frame-startup stage, the stuttering stabilization stage, and the runtime stage. Different playback stages correspond to different attribute information sets.

[0113] Optionally, in a further refined implementation of the congestion control algorithm, not only are the attribute information set of the playback client and network characteristics considered, but the playback stage is also taken as a key factor affecting the selection of the policy level and the target transmission policy.

[0114] Optionally, the identification of playback stages is based on different stages of streaming media data transmission and the real-time status of the playback client. The entire playback process can be divided into three main stages: the first frame - startup stage, the stuttering stabilization stage, and the runtime stage. Each stage has specific transmission requirements and optimization goals. The first frame - startup stage is when the playback client initially requests and receives streaming media data, with the goal of quickly filling the playback buffer and reducing the latency of the first frame playback. The stuttering stabilization stage is entered during playback if stuttering or fluctuations are detected, with the goal of restoring stable playback and minimizing the duration and frequency of stuttering. The runtime stage is the regular stage of the playback process, with the goal of optimizing transmission efficiency and network resource utilization while ensuring playback quality.

[0115] Optionally, after determining the playback stage, the process of determining the policy level can be further refined, adjusting the policy level based on the attribute information set and network characteristics of the playback stage. Different playback stages have different requirements and tolerances for transmission quality, requiring the policy level settings to be flexible and adaptable to different scenarios.

[0116] Optionally, during the first frame-startup phase, a higher strategy level is preferred to accelerate data transmission, quickly fill the buffer, and reduce first frame latency. During the stabilization phase, a moderate or conservative strategy level is adopted to ensure stable data transmission and avoid further stuttering caused by excessive network resource consumption. During runtime, the choice of strategy level becomes more dependent on real-time network characteristics and historical transmission performance, aiming to find a balance between transmission efficiency and playback quality.

[0117] Optionally, the attribute information set may include not only basic information about the client device, but also real-time status during playback, such as buffer fill level, hardware resource usage, and player playback rate. This information changes with the playback stage and is crucial for adjusting the strategy level and selecting the target transmission strategy.

[0118] Optionally, by incorporating information from the playback phase into the policy level determination process, the target transmission strategy can be selected more precisely. For example, during the first frame-start phase, strategies such as increasing the congestion window and improving the pacing rate can be chosen; during the stuttering stabilization phase, more frequent packet loss recovery mechanisms can be enabled, and slow start parameters can be adjusted to restore stable playback; during the runtime phase, the transmission strategy can be dynamically adjusted based on real-time feedback from network conditions, such as bandwidth detection and window size management, to adapt to network fluctuations.

[0119] Optionally, by incorporating dynamic changes in playback stage, attribute information set, and network characteristics into the congestion control algorithm, transmission strategies can be adjusted more intelligently to provide an optimized experience that matches the playback stage. This refined control not only reduces first-frame playback latency and lowers the frequency of stuttering, but also quickly restores transmission efficiency when network conditions change, thereby significantly improving the overall user experience and service quality.

[0120] In summary, by dynamically identifying the playback stage and updating the attribute information set, it is possible to achieve fine-grained selection of policy level and target transmission policy, ensuring that appropriate transmission policies can be provided at different stages of streaming media playback to meet specific transmission needs and optimize the playback experience, demonstrating the intelligence and adaptability of the congestion control algorithm.

[0121] As an optional implementation, in the transmission strategy set, determining the target transmission strategy that satisfies the strategy level includes: in the playback stage, determining at least a transmission parameter set associated with the strategy level, wherein different playback stages correspond to different transmission parameter sets, and the transmission parameters in the transmission parameter set are used to represent the parameters required for transmitting streaming media data under the strategy level; in the transmission strategy set, the transmission strategy that at least includes the transmission parameter set is determined as the target transmission strategy.

[0122] In this embodiment, during the process of determining the target transmission strategy that meets the policy level in the transmission strategy set, at least the transmission parameter set associated with the policy level can be determined during the playback stage. In the transmission strategy set, transmission strategies that at least include the transmission parameter set can be determined as the target transmission strategy. Different playback stages correspond to different transmission parameter sets. The transmission parameters in the transmission parameter set can be used to represent the parameters required for transmitting streaming media data under the policy level. The transmission parameter set may include parameters such as congestion window, pacing rate, and startup speed, without specific limitations here.

[0123] Optionally, in this embodiment, the selection and determination process of the target transmission strategy is more finely divided into specific playback stages, and the strategy level under each stage is closely related to a specific set of transmission parameters.

[0124] Optionally, the above process emphasizes that when determining a transmission strategy, it is necessary to consider not only the strategy level but also the specific transmission parameter set settings. Strategy levels include high-intensity, moderate, and weak network strategies, and the associated transmission parameter set contains the parameter settings required to achieve that level, such as congestion window size, pacing rate, slow start speed, and retransmission mechanism parameters. The transmission parameter set for each playback stage is carefully designed to meet the specific transmission requirements and optimization goals of that stage.

[0125] Optionally, different playback stages correspond to different sets of transmission parameters. The first frame – startup stage may require rapid buffer filling, so the corresponding transmission parameter set may include a larger congestion window and a higher pacing rate. The stuttering stabilization stage may require restoring and maintaining playback stability, so the corresponding transmission parameter set may include finer congestion window adjustments, a more conservative pacing rate, and more frequent packet loss detection and recovery mechanisms. The transmission parameter set during runtime focuses on dynamic adjustment, continuously optimizing the transmission strategy based on real-time network characteristics and performance feedback to maintain a suitable playback experience.

[0126] Optionally, within the transmission strategy set, a target transmission strategy can be selected from multiple preset strategies that contain at least a set of transmission parameters matching the current playback stage and strategy level. This means that the target transmission strategy not only has a general congestion control mechanism but also specifically integrates a set of parameters suitable for the current transmission stage and network conditions, enabling more refined transmission control.

[0127] Optionally, the transmission parameter set is not static but dynamically adjusted based on real-time network characteristics and the playback client's status. Network conditions and terminal feedback can be continuously monitored, and the parameters in the transmission parameter set can be updated in a timely manner to cope with network changes or fluctuations in client attributes, ensuring that the transmission strategy is always in an adaptive state.

[0128] Optionally, by finely selecting the transmission parameter set at specific playback stages, the data transmission process can be controlled more precisely, reducing unnecessary data retransmissions and network resource waste, and improving transmission efficiency. Simultaneously, the aforementioned transmission parameter optimization based on playback stage and policy level can provide a smooth, low-latency playback experience at different playback stages, improving user satisfaction and reducing user churn due to stuttering or first-frame delays.

[0129] In this embodiment, by tightly integrating the playback stage, policy level, and transmission parameter set, dynamic and fine-grained adjustment of the transmission policy is achieved. This ensures appropriate data transmission control under various playback scenarios, thereby improving the transmission efficiency, stability, and user experience of the streaming media playback service. This method demonstrates the adaptability and intelligence of congestion control algorithms, effectively addressing the uncertainties of the network environment and providing high-quality streaming media data transmission services to playback clients.

[0130] As an optional implementation, during the playback phase, at least a set of transmission parameters associated with the policy level is determined, including: during the playback phase, determining parameter adjustment information and a set of transmission parameters associated with the policy level, wherein the parameter adjustment information is used to represent the rules for adjusting the transmission parameters in the set of transmission parameters at the policy level; and in the transmission policy set, determining a transmission policy that includes at least the set of transmission parameters as the target transmission policy, including: in the transmission policy set, determining a transmission policy that includes parameter adjustment information and a set of transmission parameters as the target transmission policy.

[0131] In this embodiment, during the playback phase, in the process of determining the transmission parameter set associated with the policy level, parameter adjustment information and the transmission parameter set associated with the policy level can be determined. The transmission policy that includes the parameter adjustment information and the transmission parameter set in the transmission parameter set can be determined as the target transmission policy. The parameter adjustment information can be used to represent the rules for adjusting the transmission parameters in the transmission parameter data under the policy level; for example, it could be to expand the congestion window, increase the pacing rate, or adjust the slow start speed, etc., without specific limitations here.

[0132] Optionally, this embodiment further refines the transmission strategy selection process, especially the determination of the target transmission strategy for a specific playback stage. It not only considers the static transmission parameter set, but also introduces dynamic parameter adjustment information to adapt to real-time changes in network conditions and playback requirements.

[0133] Optionally, parameter adjustment information is a rule or instruction used to guide how to dynamically adjust parameters in the transmission parameter set to adapt to a specific policy level and playback stage. For example, under a high-policy-level, parameter adjustment information may instruct the expansion of the congestion window, the increase of the pacing rate, and the adjustment of the slow start speed to fully utilize network resources and quickly fill the playback buffer. Under a weak-network policy level, parameter adjustment information may suggest reducing the congestion window, decreasing the pacing rate, and extending the slow start time to avoid excessive bandwidth consumption and reduce packet loss and retransmissions.

[0134] Optionally, each playback stage (first frame-start stage, stuttering stabilization stage, runtime stage, etc.) has specific transmission requirements and optimization goals. In this implementation, the parameter adjustment information is adjusted according to the characteristics of the playback stage. For example, in the first frame-start stage, it may be necessary to quickly initialize the connection and send a large amount of data, so the parameter adjustment information will tend to increase the transmission rate; while in the stuttering stabilization stage, more refined congestion management may be needed, so the parameter adjustment information will focus more on improving the stability and accuracy of data transmission.

[0135] Optionally, within the transmission strategy set, transmission strategies that contain a set of transmission parameters and their adjustment information that match the current playback stage and strategy level can be selected and identified as the target transmission strategy. This means that the target transmission strategy not only presets a set of static transmission parameters but also includes a set of dynamic adjustment rules, enabling it to self-correct based on real-time monitored network characteristics and playback client status, ensuring that transmission behavior matches current requirements.

[0136] Optionally, parameter adjustment information acts as a bridge in the target transmission strategy, connecting the static set of transmission parameters with the dynamically changing network environment and playback requirements. Through parameter adjustment information, transmission parameters can be flexibly adjusted to cope with network congestion, link fluctuations, and changes in client attributes, ensuring efficient and stable data transmission.

[0137] Optionally, by integrating parameter adjustment information into the target transmission strategy, changes in network characteristics and playback client status can be monitored in real time, and transmission parameters can be adjusted according to these changes to achieve adaptive optimization of transmission behavior. This dynamic adjustment capability is crucial for improving service quality, reducing stuttering and first-frame latency, and optimizing user experience.

[0138] Optionally, by finely adjusting transmission parameters and intelligently matching playback stages and network characteristics, a more stable and high-quality streaming media playback service can be provided, thereby enhancing task value and market competitiveness. The aforementioned congestion control algorithm based on dynamic parameter adjustment can effectively cope with the uncertainties of the network environment, providing users with a smooth, low-latency playback experience and meeting the needs of different scenarios.

[0139] In this embodiment, by determining parameter adjustment information and a transmission parameter set that match the policy level during the playback phase, dynamic optimization and fine-grained control of the transmission policy are achieved. This method not only demonstrates the intelligence and adaptability of congestion control algorithms but also significantly improves the transmission efficiency and user experience of streaming media services, which is of great significance for service quality optimization in highly interactive scenarios such as live streaming.

[0140] As an optional implementation, the streaming media data includes a first frame, and the playback stage includes a start playback stage for playing the first frame. During the playback stage, at least a set of transmission parameters associated with the policy level is determined, including: during the start playback stage, at least a set of transmission parameters associated with the policy level is determined, wherein the set of transmission parameters includes at least one of the following transmission parameters: the first window size corresponding to the first frame, the start speed of the first frame, and the start characteristic parameters of the first frame, wherein the duration required to transmit the first frame according to the transmission parameters in the set of transmission parameters is shorter than the duration required to transmit the streaming media data according to the transmission strategies in the set of transmission strategies other than the target transmission strategy.

[0141] In this embodiment, during the playback phase, in determining the set of transmission parameters associated with the policy level, at least the set of transmission parameters associated with the policy level can be determined during the playback startup phase. The streaming media data may include the first frame. The playback phase may include a playback startup phase for playing the first frame. The transmission parameter set may include at least one of the following transmission parameters: the first window size corresponding to the first frame, the startup speed of the first frame, and the startup characteristic parameters of the first frame. The time required to transmit the first frame according to the transmission parameters in the transmission parameter set is shorter than the time required to transmit streaming media data according to a transmission strategy other than the target transmission strategy in the transmission policy set. The first window corresponding to the first frame may be a congestion control window. The startup speed may be a slow startup speed.

[0142] Optionally, this implementation focuses on the start-up playback phase of streaming media playback, particularly optimizing the transmission of the first frame. The goal is to provide faster and more efficient data transmission during this critical phase to reduce perceived latency and improve the first frame playback experience.

[0143] Optionally, at the beginning of streaming playback, a startup playback phase can be identified and initiated. The main task of the startup playback phase is to quickly transmit the first frame of data so that the user can quickly see the start of the content and reduce waiting time. Identification of the startup playback phase is typically based on the client's request pattern and historical playback behavior. During the startup playback phase, an appropriate set of transmission parameters can be determined according to the current policy level, including but not limited to the first frame's window size, the first frame's startup speed, and the first frame's startup characteristic parameters. The settings of these parameters directly affect the efficiency and speed of the first frame transmission. For example, the first window size (i.e., the congestion control window) determines the amount of data that can be sent in the early stages of connection establishment; a larger first window size helps to fill the client's buffer faster and reduce first frame latency. The startup speed (slow startup speed) and startup characteristic parameters affect the rate and pattern of initial data transmission; a higher startup speed and more aggressive startup characteristics can accelerate the transmission of the first frame of data.

[0144] Optionally, strategies that contain a set of transmission parameters matching the current strategy level are selected from the transmission strategy set and used as the target transmission strategy. This means that the target transmission strategy will take into special consideration the transmission efficiency and user experience during the playback startup phase, and by optimizing the first frame transmission parameters, ensure that the first frame arrives at the client faster than under other transmission strategies.

[0145] Optionally, transmitting the first frame according to the transmission parameter set in the target transmission strategy can significantly shorten the transmission time. This is because the transmission parameter set in the target transmission strategy is carefully designed for the playback startup phase and strategy level, efficiently utilizing network resources and reducing data retransmission and waiting time, thereby achieving shorter latency in the first frame transmission. The above method, by adjusting transmission parameters in real time, ensures that streaming media data can be delivered to the user terminal as quickly as possible during the playback startup phase.

[0146] Optionally, the time required to transmit the first frame according to the transmission parameter set in the target transmission strategy is significantly shorter than the time required to transmit according to other non-target strategies in the transmission strategy set. This means that even under less than ideal network conditions, the target transmission strategy can achieve rapid transmission of the first frame data by dynamically adjusting the transmission parameters, providing users with a faster first frame playback experience.

[0147] Optionally, optimizing the first frame transmission time is crucial for improving the user experience of live streaming and streaming services. Users can quickly see video content when starting playback, reducing "black screen" waiting time and increasing satisfaction. Furthermore, faster first frame transmission helps reduce network bandwidth consumption and retransmission frequency, thereby saving operating costs and having a positive impact on the long-term development of the service.

[0148] In this embodiment, by determining a set of transmission parameters matching the policy level during the playback initiation phase, particularly optimizing the parameters for the first frame transmission, the playback speed of the first frame can be significantly improved, latency reduced, user experience enhanced, and task competitiveness increased while ensuring data transmission quality and stability. This method demonstrates the efficiency and intelligence of congestion control algorithms in real-time interactive scenarios and is one of the key technologies for improving streaming media service quality.

[0149] As an optional implementation, the playback phase includes a stable playback phase for streaming media data. During the playback phase, at least one set of transmission parameters associated with the policy level is determined, including: during the stable playback phase, at least one set of transmission parameters associated with the policy level is determined, wherein the set of transmission parameters includes at least one of the following transmission parameters: frame linkage parameters, bandwidth detection parameters, window control parameters, transmission rate control parameters, filtering threshold, and sampling parameters. The stuttering performance achieved by transmitting streaming media data according to the transmission parameters in the transmission parameter set is lower than the stuttering performance achieved by transmitting streaming media data according to transmission strategies other than the target transmission strategy in the transmission strategy set.

[0150] In this embodiment, during the playback phase, the set of transmission parameters associated with the policy level is determined. During the stable playback phase, the set of transmission parameters associated with the policy level can also be determined. The playback phase can include a stable playback phase for streaming media data. The set of transmission parameters can include at least one of the following transmission parameters: frame linkage parameters, bandwidth detection parameters, window control parameters, transmission rate control parameters, filtering thresholds, and sampling parameters. Frame linkage parameters can be frame linkage amplitude / frequency. Bandwidth detection parameters can be bandwidth detection amplitude. Window control parameters can be congestion window gain amplitude. Transmission rate control parameters can be Pacing gain amplitude. Filtering thresholds can be low-pass filter thresholds. Sampling parameters can be sampling frequency or sampling thresholds. The stuttering performance achieved by transmitting streaming media data according to the transmission parameters in the set of transmission parameters is lower than the stuttering performance achieved by transmitting streaming media data according to transmission strategies other than the target transmission strategy.

[0151] Optionally, this implementation focuses particularly on the stable playback phase of streaming media data playback. Its goal is to optimize stuttering performance during this phase by dynamically adjusting transmission parameters, ensuring smooth transmission of streaming media data and reducing playback interruptions perceived by the user.

[0152] Optionally, the stable playback phase refers to the stage in the streaming media data playback process where the first frame has been completed, and continuous playback maintains a smooth playback state. The challenge in this phase lies in how to continuously optimize the transmission strategy, reduce stuttering and latency, and maintain playback quality under changing network conditions.

[0153] Optionally, during the stable playback phase, at least a set of transmission parameters associated with the current policy level can be determined. This set of transmission parameters includes a series of parameters used to optimize data transmission. Specific parameters include frame linkage parameters, bandwidth detection parameters, window control parameters, transmission rate control parameters, filtering thresholds, and sampling parameters, etc.

[0154] For example, each set of parameters targets a different transmission optimization objective. For instance: frame linkage parameters (e.g., frame linkage amplitude / frequency) optimize the transmission order and priority of video frames, ensuring key frames are transmitted first and improving playback smoothness. Bandwidth detection parameters (e.g., bandwidth detection amplitude) are used to understand available bandwidth in real time and dynamically adjust the transmission rate to avoid excessive bandwidth consumption leading to stuttering. Window control parameters (e.g., congestion window gain amplitude) control the size of the congestion window, balancing data transmission volume and network congestion to reduce packet loss. Transmission rate control parameters (e.g., Pacing gain amplitude) adjust the data transmission rate to ensure data transmission matches network conditions and reduce stuttering. Filtering thresholds (e.g., low-pass filter thresholds) filter out fluctuating network signals to avoid stuttering caused by instantaneous fluctuations. Sampling parameters (e.g., sampling frequency or sampling threshold) monitor network status to ensure real-time adjustment of transmission strategies, reducing stuttering and latency.

[0155] Optionally, by applying the transmission parameter set in the target transmission strategy, better stuttering performance indicators can be achieved. That is, when transmitting streaming media data during a stable playback phase, key performance indicators such as the number of stutters and stuttering duration will be lower than those using other non-target transmission strategies. This is because the parameter set under the target transmission strategy is carefully designed and adjusted according to real-time network characteristics and strategy level, which can more effectively cope with network fluctuations, reduce data retransmissions, optimize data transmission efficiency, and thus significantly reduce stuttering.

[0156] Optionally, determining the transmission parameter set and its application in the target transmission strategy is a dynamic and adaptive process. Network status and playback client feedback can be continuously monitored, and the aforementioned parameters can be adjusted in real time according to changing environments and needs, ensuring that the transmission strategy always meets requirements and can cope with potential network congestion, link fluctuations, and changes in device performance.

[0157] Optionally, optimizing performance metrics during stable playback is crucial for improving streaming service quality and enhancing user experience. A smoother, less interrupted playback experience not only increases user satisfaction and reduces churn, but also enhances the competitiveness and appeal of the service.

[0158] In this embodiment of the application, by determining a set of transmission parameters that match the policy level during the stable playback phase, especially the parameter adjustments for optimizing stuttering performance, the transmission efficiency and stability of streaming media data can be continuously optimized in a dynamically changing network environment, reducing stuttering and significantly improving the playback experience and service quality.

[0159] As an optional implementation, step S202, in response to a request from the playback client, obtains the attribute information set of the playback client from the target request, including: in response to a target request from the playback client during the playback phase, obtaining the attribute information set of the playback client during the playback phase from the target request.

[0160] In this embodiment, during the process of obtaining the attribute information set of the playback client from the target request in the request from the playback client, the target request of the playback client during the playback stage can be detected. If the target request is detected, the attribute information set of the playback client during the playback stage can be obtained from the target request.

[0161] Alternatively, this implementation emphasizes how to respond to requests from the playback client at different stages of streaming media playback and obtain attribute information sets to further optimize the transmission strategy.

[0162] Optionally, when the playback client sends a target request, it can detect whether the target request belongs to a specific playback stage, such as the first frame-startup stage, the stuttering stabilization stage, or the runtime stage. Target request detection can be based on information such as request metadata, request frequency, and request data volume, and the characteristics of the target request can be analyzed to determine the current playback stage.

[0163] Optionally, once the target request is identified as belonging to a specific playback stage, the attribute information set of the playback client can be extracted from that target request. This attribute information set includes various attributes of the playback client, such as player buffer size, device type, hardware quality, network status, and operating system type. This information is crucial for developing the most suitable transmission strategy and helps understand the client's specific needs and capabilities.

[0164] Optionally, the playback client's attribute information can change at different stages of playback, such as fluctuations in network quality or changes in the player's cache fill level. The system can detect and update the attribute information set in real time, ensuring that transmission strategies are formulated based on the latest client status, thereby achieving more precise transmission optimization.

[0165] Optionally, after obtaining the attribute information set of the playback client, the transmission parameter set can be dynamically adjusted based on the aforementioned attribute information, combined with the current network characteristics and policy level, to select the transmission strategy most suitable for the current playback stage and client state. For example, during the playback startup stage, if the attribute information set indicates that the client device has strong performance and good network conditions, an aggressive transmission strategy can be selected to quickly fill the playback buffer; while during the stabilization stage, a more conservative strategy can be selected based on the network state and device performance in the attribute information set to ensure playback stability.

[0166] Optionally, by responding to playback client requests in real time and extracting attribute information sets to optimize transmission strategies, a higher quality streaming media playback experience can be provided. This not only reduces playback latency and stuttering but also improves playback smoothness, enhancing user satisfaction and thus bringing greater value to the task. Furthermore, this attribute information set-based strategy optimization helps maintain stable playback service under different network conditions, reducing service quality degradation caused by network fluctuations.

[0167] In this embodiment, by detecting the target requests of the playback client at a specific playback stage and obtaining a real-time attribute information set, dynamic optimization of the transmission strategy is achieved. This method ensures that the transmission strategy can be adjusted according to the real-time status of the playback client and the needs of a specific playback stage, thereby providing more efficient and stable data transmission and significantly improving the user experience of streaming media playback, which is particularly important for live streaming and highly interactive streaming media services.

[0168] As an optional implementation, the streaming media data includes the first frame, the playback stage includes the start playback stage of the first frame, and in response to a target request from the playback client during the playback stage, the attribute information set of the playback client during the playback stage is obtained from the target request, including: in response to the target request sent by the playback client during the start playback stage, obtaining the attribute information set of the playback client during the start playback stage from the target request, wherein the attribute information set includes at least one of the following: the type of operating system of the terminal device, the maximum amount of data that the playback client is allowed to receive, the minimum buffer size of the playback client, and the quality index of the terminal device.

[0169] In this embodiment, during the process of obtaining the attribute information set of the playback client during the playback phase from the target request, if a target request sent by the playback client during the playback initiation phase is detected, the attribute information set of the playback client during the playback initiation phase can be obtained from the target request. The attribute information set includes at least the following attribute information: the type of operating system on the terminal device, the maximum amount of data the playback client is allowed to receive, the minimum buffer size of the playback client, and the quality indicators of the terminal device. The operating system can also be referred to as the terminal operating system. The maximum amount of data that can be received can be the number of congestion windows (CWNDs). The minimum buffer size can be the client's minimum buffer size. The quality indicators of the terminal device can be used to represent the quality of the terminal hardware.

[0170] Optionally, the above embodiments further illustrate how to obtain key attribute information from the target request of the playback client during streaming media data transmission, especially during the first frame playback start stage, and thereby optimize the transmission strategy.

[0171] Optionally, when the playback client begins requesting streaming media data, especially the first frame, this can be detected and identified as a target request for initiating playback. These stages are crucial for optimizing the streaming media playback experience, as they directly relate to the latency and smoothness of the user's initial encounter with the content.

[0172] Optionally, the attribute information set of the playback client during the playback initiation phase can be obtained from the detected target requests. This attribute information includes, but is not limited to: the type of operating system on the terminal device (different operating systems may have different handling methods for data transmission and cache management, affecting transmission efficiency); the maximum amount of data the playback client is allowed to receive (the number of notification windows), which determines the maximum amount of data that can be sent at the initial connection establishment stage and directly affects the first frame transmission speed; the minimum cache size of the playback client (client minimum cache), understanding the minimum cache requirements ensures that data transmission can meet playback smoothness requirements and reduces stuttering; and the quality indicators of the terminal device, such as the performance of the Central Processing Unit (CPU) and memory size, which affect data processing and decoding speed, thus impacting the playback experience.

[0173] Optionally, after obtaining the attribute information set, the attribute information in the set can be analyzed and combined with the real-time network conditions to optimize the transmission parameter set. For example, if the client's operating system type indicates that it has an efficient cache management mechanism, a higher number of announcement windows and a faster slow start speed can be adopted; if the terminal device's quality indicators show strong hardware performance, a more aggressive transmission strategy can be selected, such as expanding the congestion window and increasing the pacing rate, to fully utilize the device's performance.

[0174] Optionally, based on the playback client's attribute information set, the transmission strategy can be intelligently adjusted to ensure that the first frame of data can be transmitted to the client in the shortest possible time, while also guaranteeing the stability and smoothness of subsequent data transmission. This adjustment is dynamic, capable of optimizing transmission behavior in real time based on changes in real-time network conditions and client status.

[0175] Optionally, by obtaining playback client attribute information from the target request and optimizing the transmission strategy based on this information, the transmission speed of the first frame can be significantly improved, startup latency reduced, and a low stuttering rate maintained during subsequent playback, thus enhancing playback smoothness. This not only increases user satisfaction but also improves the service quality and market competitiveness of the task.

[0176] In this embodiment, by detecting and responding to the target request from the playback client during the playback initiation phase, and extracting a detailed set of attribute information from the request, personalized and intelligent adjustments to the transmission strategy are achieved. This method ensures a high-quality streaming media playback experience under different device and network conditions. In particular, the optimization of first-frame transmission is a key technology for improving the user experience of live streaming services and highly interactive streaming media applications.

[0177] As an optional implementation, the playback stage includes a stable playback stage for streaming media data. In response to a target request from the playback client during the playback stage, the attribute information set of the playback client during the playback stage is obtained from the target request. This includes: in response to a target request sent by the playback client during the stable playback stage, obtaining the attribute information set of the playback client during the stable playback stage from the target request, wherein the attribute information set includes at least one of the following: the type of operating system of the terminal device, the minimum buffer size of the playback client, the bitrate category of the playback client, and the quality index of the terminal device.

[0178] In this embodiment, during the process of obtaining the attribute information set of the playback client during the playback phase from the target request, if the playback client sends the target request during the stable playback phase, the attribute information set of the client during the stable playback phase can be obtained from the target request. The attribute information set includes at least one of the following attribute information: the type of operating system on the terminal device, the minimum buffer size of the playback client, the bitrate category of the playback client, and the quality index of the terminal device. The bitrate category of the playback client can be the application bitrate category.

[0179] Optionally, the above implementation focuses on transmission optimization during the stable playback phase of streaming media data, involving extracting key attribute information from the target request of the playback client to dynamically adjust the transmission strategy and ensure smooth and efficient playback.

[0180] Optionally, it can be determined whether playback is in a stable phase. This phase typically occurs after the first frame of data is successfully transmitted, at which point playback enters a continuous and stable state. Whether a stable playback phase has been entered is determined by monitoring the pattern, frequency, and data volume of playback requests, as well as client feedback.

[0181] Optionally, after identifying a target request for a stable playback phase, the attribute information set of the playback client can be extracted from the target request. This attribute information set includes important parameters used to assess the capabilities and requirements of the client device, as well as the current playback environment. These attributes include: the type of operating system running on the terminal device; different operating systems may have specific requirements or limitations on transmission, affecting the formulation of transmission strategies; the minimum buffer size of the playback client (i.e., the client's minimum buffer size), reflecting the minimum amount of data buffering the client can accept to maintain playback stability; the bitrate category of the playback client (application bitrate category), which reflects the quality and transmission requirements of streaming media data and is an important basis for formulating transmission strategies; and the quality indicators of the terminal device, which may include the device's CPU performance, memory size, screen resolution, etc., used to judge the device's processing power and potential for playback smoothness.

[0182] Optionally, the acquired attribute information set will be analyzed to adjust the transmission parameter set, including but not limited to frame linkage parameters, bandwidth probing parameters, window control parameters, transmission rate control parameters, filtering thresholds, and sampling parameters. For example, if the attribute information set shows that the terminal device has a high operating system type and quality index, the bandwidth probing amplitude can be increased to more actively utilize network resources; if the minimum buffer size of the playback client is large, the gain amplitude of the congestion window can be appropriately adjusted to maintain a high data transmission rate without causing stuttering.

[0183] In this embodiment, based on the analysis of attribute information sets, transmission strategies can be adjusted in real time to ensure that data transmission matches the current playback environment and client capabilities. This method can more intelligently respond to network fluctuations and device performance changes, reducing stuttering and latency, and improving playback quality. By optimizing transmission strategies according to attribute information sets during stable playback, playback smoothness can be improved, user-perceived interruptions reduced, and user experience enhanced. Simultaneously, this intelligent adjustment also improves transmission efficiency, reduces resource waste, and has a positive impact on task operation costs and service quality.

[0184] In summary, by obtaining the playback client's attribute information set from the target request during the stable playback phase and dynamically adjusting the transmission strategy based on this information, streaming media data transmission optimization is achieved. This method ensures a high-quality playback experience even under changing network and device conditions.

[0185] As an optional implementation, in the process of transmitting streaming media data to the playback client according to the target transmission strategy, the method further includes: obtaining the update results of attribute information set and / or network characteristics; determining the matching degree between the update results and the target transmission strategy; and determining an update strategy for the target transmission strategy based on the matching degree, wherein the update strategy is used to represent the rule for whether to update the target transmission strategy.

[0186] In this embodiment, during the process of transmitting streaming media data to the playback client according to the target transmission strategy, the target transmission strategy can be periodically self-adjusted. During this process, update results of attribute information sets and / or network characteristics can be obtained. The matching degree between the update results and the target transmission strategy can be determined. Based on the matching degree, an update strategy for updating the target transmission strategy can be determined, where the update strategy represents the rule for whether to update the target transmission strategy. The matching degree can be considered as the degree of strategy adaptation.

[0187] Optionally, this implementation describes a dynamic adjustment mechanism for intelligently adjusting the transmission strategy based on updates to playback client attribute information and changes in network characteristics during streaming media data transmission, in order to continuously optimize the playback experience.

[0188] Optionally, during streaming media data transmission, updates to the playback client's attribute information set can be obtained periodically or event-driven, including the type of operating system running on the terminal device, minimum buffer size, bitrate category, and device quality indicators. Simultaneously, real-time changes in network characteristics can be monitored, such as packet loss rate, minimum RTT time, and link utilization. These updated results reflect the latest state of the current playback environment and are crucial for adjusting transmission strategies.

[0189] Optionally, based on the obtained update results, the impact of the aforementioned changes on the current target transmission strategy can be evaluated. The calculation of the matching degree or strategy adaptability can be based on various factors, such as the magnitude of changes in the attribute information set, the stability or volatility of network characteristics, and the adaptability and flexibility of the transmission strategy. If the update results highly match the target transmission strategy, i.e., the changes in the playback client and network conditions do not deviate from the expectations of the current strategy, the matching degree will be high; conversely, if significant changes occur, such as a decline in network quality or fluctuations in client hardware performance, the matching degree will decrease accordingly.

[0190] Optionally, the update strategy is a rule or algorithm used to determine whether and how to update the current target transmission strategy. Based on matching degree analysis, if the matching degree is lower than a preset threshold, it indicates that the current strategy is no longer suitable for the changing playback environment, and an update strategy can be triggered to adjust the strategy. The update strategy can include: keeping the current strategy unchanged: if the matching degree is high enough, it indicates that the current strategy can still effectively cope with the playback environment, and no change is needed. Minor adjustments: if the matching degree has decreased but is still within an acceptable range, some parameters can be slightly adjusted, such as reducing the bandwidth probe amplitude or adjusting the congestion window size. Major adjustments: if the matching degree has dropped significantly, more aggressive strategy adjustments can be implemented, such as switching to a different congestion control algorithm or reducing the transmission rate, to cope with sudden changes in network conditions or fluctuations in client device performance.

[0191] Optionally, after determining the update strategy, corresponding strategy adjustments can be performed to update the transmission parameter set in real time, ensuring that the streaming media data transmission strategy matches the latest attribute information of the playback client and real-time network characteristics. This self-adjusting capability enables the system to dynamically respond to various changes during playback and maintain high-quality transmission service.

[0192] Optionally, by periodically adjusting the target transmission strategy based on updates to the attribute information set and network characteristics, adaptability to changing environments is improved, enabling more flexible responses to scenarios such as network congestion, device performance fluctuations, and changes in client caching requirements. This intelligent adjustment mechanism helps continuously optimize the playback experience, reduce stuttering and latency, and improve user satisfaction.

[0193] In this embodiment, the aforementioned dynamic adjustment mechanism dynamically determines the degree of strategy adaptation by real-time monitoring and analysis of the update results of playback client attribute information and network characteristics, and intelligently adjusts the transmission strategy to maintain the efficiency and stability of streaming media data transmission. This method ensures that even if the playback environment changes during playback, timely strategy adjustments can be made, providing a good playback experience.

[0194] As an optional implementation, determining an update strategy for the target transmission policy based on the matching degree includes: in response to a matching degree less than a matching degree threshold, determining an update strategy as a rule that allows updating the target transmission policy; and in response to a matching degree greater than or equal to the matching degree threshold, determining an update strategy as a rule that maintains the target transmission policy.

[0195] In this embodiment, during the process of determining the update strategy for the target transmission strategy based on the matching degree, the relationship between the matching degree and the matching degree threshold can be determined. If the matching degree is less than the matching degree threshold, the update strategy can be determined to be a rule that allows updating the target transmission strategy. If the matching degree is greater than or equal to the matching degree threshold, the update strategy can be determined to be a rule that maintains the target transmission strategy.

[0196] Optionally, this implementation proposes a mechanism for dynamically updating or maintaining the target transmission strategy based on matching degree, which aims to ensure that the streaming media data transmission strategy can adapt to changes in playback client attribute information and network characteristics in real time, thereby optimizing the playback experience.

[0197] Optionally, the matching threshold is a predefined value used to determine the degree of compatibility between the current transmission strategy and the playback client's attribute information and network characteristics. Setting the matching threshold requires comprehensive consideration of playback stability, smoothness requirements, and the rational utilization of network resources, and can be determined based on task requirements and performance metrics.

[0198] Optionally, the matching degree between the current transmission strategy and the latest attribute information set and network characteristics can be calculated periodically. If the matching degree is lower than a preset threshold, it indicates that the current strategy may no longer be suitable for the current playback environment and there is room for optimization. Conversely, if the matching degree is higher than or equal to the threshold, it indicates that the current strategy can effectively meet the playback requirements and does not need to be adjusted immediately.

[0199] Optionally, when the matching degree is less than the matching degree threshold, the update policy is determined to be a rule that allows updates to the target transmission policy. This means that the transmission parameter set will be adjusted based on the latest acquired attribute information set and network characteristics to optimize data transmission. Updates may include adjusting the congestion window size, changing the pacing rate, adjusting the bandwidth probe amplitude, etc., to cope with network fluctuations or changes in client device performance.

[0200] Optionally, when the matching degree is greater than or equal to the matching degree threshold, the update strategy is determined to be the rule that maintains the target transmission strategy. In the above case, it indicates that the current transmission strategy is sufficiently adapted to the playback environment, and there is no need to update the strategy immediately. This avoids unnecessary strategy adjustments, reduces transmission uncertainty, and helps maintain playback stability and smoothness.

[0201] Optionally, based on the above judgment, an corresponding update strategy can be executed. If the update strategy is determined to allow updates, the transmission strategy can be adjusted immediately to cope with the changing playback environment; if the update strategy is to maintain the current strategy, the current transmission strategy can continue to be used, while monitoring changes in the playback environment is maintained, and preparations can be made to adjust the strategy when the matching degree decreases.

[0202] In this embodiment, the dynamic update mechanism based on matching degree improves the intelligence and dynamic adaptability of the transmission strategy. It can maintain playback quality while rationally utilizing network resources, especially when network conditions change or the performance of the playback client device fluctuates. This allows for timely adjustments to the transmission strategy, preventing playback interruptions and stuttering, and improving the user experience. In summary, by setting a matching degree threshold and using a matching degree-based update strategy, the streaming media data transmission strategy is ensured to be intelligently updated or maintained according to changes in playback client attribute information and network characteristics, maintaining high transmission efficiency and stability while optimizing the playback experience.

[0203] As an optional implementation, in response to a matching degree less than a matching degree threshold, determining an update policy as a rule that allows updating the target transmission policy includes: in response to a matching degree less than a matching degree threshold, determining an update policy as a rule that allows updating the policy level of the target transmission policy.

[0204] In this embodiment, when the matching degree is less than the matching degree threshold, the update strategy can be determined as the rule by which Yunxi updates the policy level of the target transmission policy. For example, the policy level can be downgraded, that is, the policy can be downgraded.

[0205] Optionally, this implementation describes how, during the streaming media data transmission process, when the matching degree between the current transmission strategy and the playback client attribute information and network characteristics is detected to be lower than a preset threshold, the transmission effect can be optimized by adjusting the level of the transmission strategy to ensure the stability and smoothness of playback.

[0206] Optionally, the policy level can be understood as the "aggressiveness" of the transmission strategy or its utilization of network resources. Generally, the higher the policy level, the more aggressive the transmission strategy, such as higher data transmission rates, larger congestion windows, and faster slow start processes; the lower the policy level, the more conservative the transmission strategy, and the smoother and more controllable the data transmission.

[0207] Optionally, when the calculated matching degree is less than a preset matching degree threshold, it indicates that the currently adopted high strategy level may no longer be suitable for the current playback environment or network conditions. For example, if there is severe network congestion or the performance of the client device degrades, the original aggressive transmission strategy may lead to data packet loss and increased latency, thereby affecting the smoothness of playback and user experience.

[0208] Optionally, if the matching degree is below a threshold, an update strategy is implemented, allowing the policy level of the target transmission strategy to be downgraded. Policy downgrading means adjusting from the current high-level policy to a lower-level policy to adapt to changing playback environments and network characteristics. For example, possible policy adjustments include reducing the congestion window size, lowering the pacing rate, and slowing down the bandwidth probing process to reduce the pressure of data transmission on the network, avoid packet loss and increased latency, thereby improving playback stability and smoothness.

[0209] Optionally, the transmission effect after policy downgrading can be monitored in real time, including key performance indicators such as packet loss rate, minimum RTT time, and stuttering rate. If the playback quality improves after policy downgrading, it indicates that the decision is reasonable; if the playback quality does not improve significantly or even deteriorates, the policy level can be further adjusted or other measures can be taken to optimize transmission.

[0210] In this embodiment, the adjustment of the policy level is a dynamic process. Based on changes in the playback environment and network characteristics, the matching degree is cyclically detected, and the policy level is upgraded or downgraded as necessary. This mechanism ensures that the transmission policy always adapts to the current playback conditions, optimizing streaming media data transmission. In summary, by monitoring the matching degree between the playback environment and network characteristics and the transmission policy, when the matching degree falls below a preset threshold, the policy level of the transmission policy can be downgraded to optimize the playback experience. This method embodies intelligent management of network resources and fine-grained control of playback quality, and is an important technical means to improve the quality of streaming media services. Through dynamic adjustment of the policy level, efficient, stable, and smooth streaming media data transmission services can be continuously provided in changing playback environments.

[0211] This application embodiment also provides a method for transmitting streaming media data from the perspective of live streaming application scenarios. Figure 3 is a flowchart of another method for transmitting streaming media data according to an embodiment of this application. As shown in Figure 3, it is applied to a content distribution network node running a live streaming protocol stack. The content distribution network node is used to transmit data with the playback client through the network and executes the following methods through the live streaming protocol stack.

[0212] Step S302: In response to the target request from the playback client, the attribute information set of the playback client is parsed from the target request. The target request is used to request the transmission of streaming media data in the live streaming service scenario. The attribute information set includes at least one attribute information of the playback client during operation, and / or at least one attribute information of the terminal device on which the playback client is installed during operation.

[0213] Step S304: In the transmission strategy set, determine the target transmission strategy that matches the attribute information set and the network characteristics of the network. The transmission strategy in the transmission strategy set is used to represent the rules for data transmission by the content distribution network nodes in the live streaming service scenario, and the network characteristics are used to represent the attributes of the network during operation.

[0214] Step S306: According to the target transmission strategy, transmit the streaming media data to the playback client, wherein the streaming media data is used for playback on the playback client.

[0215] In the embodiments of this application, the application process and function of the above solution in the live streaming application scenario are mainly reflected in the following key steps. The above steps together ensure the efficient, stable and personalized transmission of streaming media data, and greatly optimize the user's live streaming viewing experience.

[0216] Optionally, when a Content Delivery Network (CDN) node receives a target request from a playback client, it parses the playback client's attribute information set from the target request. This attribute information set contains attributes of the playback client during operation, such as operating system type, minimum cache requirements, bitrate preferences, and terminal device quality metrics. This step allows the CDN node to understand the client's specific needs and current status.

[0217] Optionally, based on the parsed attribute information set and real-time network characteristics, CDN nodes select the target transmission strategy that best matches the current context from a predefined set of transmission strategies. The transmission strategy set is a collection of rules for data transmission by CDN nodes in different live streaming service scenarios, with each strategy optimized for specific network conditions and client attributes. Network characteristics include packet loss rate, latency, and bandwidth utilization, reflecting the real-time network status.

[0218] Optionally, once the target transmission strategy is determined, CDN nodes transmit streaming media data to the playback client according to this strategy. This ensures that the data transmission rate and efficiency are adapted to the client's capabilities and network conditions, thereby enabling smooth live streaming playback on the client.

[0219] Optionally, by parsing the playback client's attribute information, CDN nodes can tailor transmission strategies for each user, providing personalized live streaming services. This means that even under different network conditions and device types, each user can obtain an optimized streaming media transmission experience. The determination of the target transmission strategy takes network characteristics into account, enabling CDN nodes to intelligently adjust bandwidth usage, window size, and transmission rate, effectively addressing network congestion, reducing stuttering and latency, and improving the smoothness and stability of live streaming. Allowing CDN nodes to dynamically adjust transmission strategies based on real-time network characteristics and client attributes ensures efficient resource utilization in a constantly changing network environment, avoiding unnecessary bandwidth waste, while ensuring the timeliness and reliability of data transmission.

[0220] In summary, the embodiments of this application significantly improve the user experience of live streaming services. Users will experience a significant improvement in viewing smoothness, playback quality, and response speed, especially in environments with poor network conditions.

[0221] Through steps S302 to S306 of this application, by introducing refined client attribute information parsing and a dynamic transmission strategy adjustment mechanism based on network characteristics into the live streaming service, efficient and stable transmission of streaming media data is achieved. This solves the common problems of stuttering, latency, and resource waste in traditional streaming media transmission, providing users with a smooth, high-quality live streaming viewing experience, while also improving the resource utilization efficiency of CDN nodes and the competitiveness of the live streaming service. Thus, the technical effect of improving the transmission quality of streaming media data is achieved, solving the technical problem of poor transmission quality of streaming media data.

[0222] This application embodiment also provides a method for transmitting streaming media data from the Software as a Service (SaaS) side. Figure 4 is a flowchart of another streaming media data transmission method according to an embodiment of this application. As shown in Figure 4, the method is applied to a server running a protocol stack. The server is used to transmit data with the playback client over the network and executes the following methods through the protocol stack.

[0223] Step S402: Obtain the target request from the playback client by calling the first interface, wherein the first interface includes a first parameter and the parameter value of the first parameter is the target request.

[0224] Step S404: In response to the target request, obtain the attribute information set of the playback client from the target request, wherein the target request is used to request the transmission of streaming media data in the playback service scenario, and the attribute information set includes at least one attribute information of the playback client during operation, and / or at least one attribute information of the terminal device on which the playback client is installed during operation.

[0225] Step S406: In the transmission strategy set, determine the target transmission strategy that matches the attribute information set and the network characteristics of the network. The transmission strategy in the transmission strategy set is used to represent the rules for data transmission by the server in the playback service scenario, and the network characteristics are used to represent the attributes of the network during operation.

[0226] Step S408: By calling the second interface, the streaming media data is transmitted to the playback client according to the target transmission strategy. The second interface includes a second parameter, the value of which is the target transmission strategy. The streaming media data is used for playback on the playback client.

[0227] Through steps S402 to S408 of this application, a target request from the playback client is obtained by calling the first interface; in response to the target request, the attribute information set of the playback client is obtained from the target request; in the transmission strategy set, a target transmission strategy matching the attribute information set and the network characteristics of the network is determined; and by calling the second interface, the streaming media data is transmitted to the playback client according to the target transmission strategy. This achieves the technical effect of improving the transmission quality of streaming media data and solves the technical problem of poor transmission quality of streaming media data.

[0228] According to an embodiment of this application, a streaming media data transmission system is also provided. Figure 5 is a schematic diagram of a streaming media data transmission system according to an embodiment of this application. As shown in Figure 5, the streaming media data transmission system 500 may include: a playback client 501 and a server 502 running a protocol stack. The server 502 is used to transmit data with the playback client 501 via a network.

[0229] The playback client returned a 501 error, used to send a target request.

[0230] Server 502 is used to respond to a target request through the protocol stack, obtain the attribute information set of the playback client from the target request; determine a target transmission strategy that matches the attribute information set and the network characteristics of the network in the transmission strategy set; and transmit the streaming media data to the playback client according to the target transmission strategy; wherein, playback client 501 is used to play the streaming media data.

[0231] In this embodiment, a streaming media data transmission system is provided. A target request is sent by a playback client 501. A server 502 responds to the target request via a protocol stack, obtaining the playback client's attribute information set from the request; in a transmission strategy set, it determines a target transmission strategy that matches the attribute information set and the network characteristics of the network; according to the target transmission strategy, the streaming media data is transmitted to the playback client; and the streaming media data is played by the playback client 501, thereby achieving the technical effect of improving the transmission quality of streaming media data and solving the technical problem of streaming media data transmission quality.

[0232] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application, such as the data used for testing, are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0233] Currently, competition among CDN vendors for customer service quality (especially in streaming media) is becoming increasingly fierce. Furthermore, trade-offs between quality and cost in different tasks can effectively translate quality gains into cost reduction gains. Therefore, universally applicable quality optimization at the protocol stack level is particularly important and represents one of the industry's core technological barriers. Consequently, the technical problem of low transmission quality for streaming media data persists.

[0234] Furthermore, in the field of live streaming protocol stack acceleration, this application provides a self-regulating congestion control method based on network characteristics and combining end-edge approaches. By introducing various types of information from the terminal side, it designs appropriate transmission strategies under different characteristics and makes adaptive decisions, achieving comprehensive optimization of all core service quality evaluation indicators. This significantly improves the performance of live streaming services, particularly reducing buffering and first-frame playback. Thus, it achieves the technical effect of improving the transmission quality of streaming media data and solves the technical problem of poor streaming media data transmission quality.

[0235] The method described in this embodiment will be further described below.

[0236] In this embodiment, Figure 6 is a schematic diagram of a CDN protocol stack evolution architecture according to an embodiment of this application. As shown in Figure 6, the key evolution points of the CDN protocol stack are illustrated below. Through several key evolution stages and technical components, it demonstrates the transformation process from data transmission optimization to product experience transmission optimization, and how these transformations can improve the quality of streaming media and live streaming services. Data transmission optimization includes the super algorithm stage, the task differentiation stage, and the network characteristic stage. Product experience transmission optimization can include the network cross-layer stage, the end-edge collaboration stage, and the real-time interaction stage such as real-time communication / real-time streaming media (RTC / RTS). Among them, the super algorithm stage mainly focuses on the optimization of transmission efficiency and data layer. Super algorithms may involve more advanced data compression, faster transmission rates, or more efficient bandwidth utilization strategies, with the focus on improving data transmission performance. The task differentiation stage optimizes for different task scenarios (e.g., video, audio, live streaming, etc.), enabling the protocol stack to adjust its transmission strategy according to task type and characteristics, providing services that are closer to task requirements. Network characterization introduces network feature detection, such as link packet loss rate, latency, and minimum RTT, enabling the protocol stack to perceive network conditions in real time and make more accurate congestion control and bandwidth management decisions. Network cross-layer implementation enables closer collaboration between different layers of the protocol stack, such as the linkage between the application layer and the network layer. This allows the protocol stack to overcome the limitations of traditional hierarchical structures, process data transmission more efficiently, and improve user experience. End-edge collaboration introduces collaborative work between the end-user (player) and the edge-side (CDN nodes). By sharing information about terminal devices and the network environment, the CDN can make more refined policy adjustments based on actual playback conditions. Real-time interaction such as RTC / RTS is deeply optimized for real-time communication / real-time streaming media services, ensuring low-latency and highly stable data transmission services in highly interactive scenarios such as video conferencing, online games, and live streaming.

[0237] Figure 7 is a schematic diagram illustrating the key points of CDN protocol stack evolution according to an embodiment of this application. As shown in Figure 7, the evolution of the CDN protocol stack from data transmission optimization to product experience-based transmission optimization covers changes in three key aspects: network transmission philosophy, architectural information entropy, and congestion control algorithms. Network transmission philosophy includes data-based network transmission and experience-based network transmission. The data-based transmission philosophy emphasizes data transmission efficiency and bandwidth utilization, primarily focusing on how transport layer protocols, such as TCP, can efficiently transmit data. Early network transmission optimization focused more on maximizing data transmission rate and throughput under limited bandwidth and network resources. With the diversification of Internet applications, especially the rise of video, audio, and real-time communication services, ensuring quality transmission has become a critical requirement. The experience-based transmission philosophy emphasizes the service quality actually perceived by users, including video smoothness, audio clarity, and interaction latency. This philosophy requires the protocol stack to consider the performance of terminal devices, the stability of the network environment, and the characteristics and needs of applications while optimizing data transmission.

[0238] Optionally, as shown in Figure 7, the architecture (information entropy) can include single kernel information entropy, application layer combined with kernel information entropy, and edge-side combined information entropy. Information entropy in single kernel information entropy can be understood as the amount of information or the complexity of the information. In the single kernel information entropy stage, the kernel protocol stack is relatively isolated, mainly relying on network layer signals (such as packet loss and latency) for congestion control and bandwidth management, with a relatively singular information source and decision-making basis. With application layer combined with kernel information entropy, as the complexity of network applications increases, relying solely on kernel layer information is insufficient to make appropriate transmission strategy decisions. In this stage, the application layer and kernel layer begin to merge. The application layer can transmit task characteristics and requirements (such as real-time performance and interactivity) to the kernel layer, which can then make more precise policy adjustments and optimizations based on this information. Edge-side combined information entropy is a newer evolutionary stage, where not only do the application layer and kernel layer work closely together, but information from the terminal side and edge side is also introduced. The concept of end-edge information entropy emphasizes that the protocol stack can perceive the status of terminal devices (such as cache size and device hardware quality) and the characteristics of edge networks (such as last-mile network quality) in real time. The addition of this information greatly increases the complexity and accuracy of decision-making, enabling the protocol stack to adjust transmission strategies in real time according to specific terminal and network conditions to achieve appropriate transmission effects and user experience.

[0239] Optionally, as shown in Figure 7, congestion control algorithms can include a fixed single algorithm, differentiated sub-algorithms, and self-decision self-regulation. A fixed single algorithm is an early congestion control algorithm, typically fixed, such as traditional TCP congestion control. It manages network traffic based on fixed rules (e.g., slow start, congestion avoidance, fast recovery), exhibiting poor adaptability and unable to cope with complex and ever-changing network environments. Differentiated sub-algorithms, with the diversification of network applications, are no longer sufficient to meet the needs of all scenarios. In the differentiated sub-algorithm stage, the protocol stack can employ different sub-algorithms for congestion control based on different task characteristics (e.g., live video streaming, gaming, file transfer) and network conditions (e.g., good, medium, poor network), improving the algorithm's flexibility and effectiveness. Self-decision self-regulation represents the final stage of evolution, where the congestion control algorithm possesses self-learning and self-regulation capabilities. In the initial connection phase, the strategy is initialized based on terminal information and network characteristics. During connection operation, the algorithm periodically adjusts its strategy according to real-time network conditions, possessing the ability to self-correct its strategy. For example, when packet loss or RTT anomalies are detected, it can automatically degrade and adjust to ensure service stability and quality.

[0240] The aforementioned evolution process reflects a significant shift from data-driven to experience-driven approaches, and from fixed rules to intelligent adjustments. This represents the core design philosophy of the third embodiment of the CDN live streaming protocol stack architecture.

[0241] Figure 8 is a schematic diagram of a first embodiment of a data-based live streaming protocol stack architecture according to an embodiment of this application. As shown in Figure 8, the first embodiment of the data-based live streaming protocol stack architecture depicts the transmission path of live streaming data from the source to the terminal player, as well as the key components and technical points involved in this process. The application layer, tengine (t-live), is located at the top of the architecture and is the task logic processing layer, mainly responsible for the encoding and decoding of the live stream, distribution strategies, task logic control, etc. tengine (t-live), as a customized application layer web server for CDN, is used to handle Hypertext Transfer Protocol (HTTP) / Hypertext Transfer Protocol Secure (HTTPS) requests, as well as other application layer task logic. The application layer and the kernel layer are connected by a dashed arrow, indicating that there is a decoupling relationship between these two layers. In the live streaming protocol stack architecture corresponding to the first embodiment, communication between tengine (t-live) and the kernel protocol stack is conducted through standard API interfaces. This decoupling design allows the application layer and transport layer to be developed and optimized independently, but it also means that the application layer cannot directly participate in the transport layer's decision-making process, reducing the flexibility and adaptability of the transmission strategy. The kernel protocol stack is the core part of the protocol stack, used to execute the Transmission Control Protocol (TCP) / Internet Protocol (IP), handle packet sending and receiving, and perform key network transmission functions such as congestion control, flow control, and error recovery. In the diagram, the kernel protocol stack is connected to the "packet loss / delay congestion signal" by a solid arrow, indicating that its transmission strategy is mainly adjusted based on network feedback signals (such as packet loss rate and latency). The packet loss / delay congestion signal describes the signals received by the kernel protocol stack when packet loss or increased latency occurs in the network. These signals are the input to the kernel congestion control algorithm, used to determine whether network congestion has occurred, thereby deciding whether to reduce the sending rate or take other congestion avoidance measures.

[0242] Optionally, as shown in Figure 8, the signal is connected to the "last-mile network" via a dashed arrow, indicating that the signal is a direct feedback of packet loss and latency in the "last-mile network," directly impacting the transmission strategy. The last-mile network refers to the portion of the network closest to the end user, typically the network of an Internet Service Provider (ISP). The performance of this part of the network directly affects the end user's experience. In Figure 8, the "last-mile network" is connected to the "client terminal player" via a dashed arrow, indicating that it is the final segment of data transmission, directly affecting the quality and efficiency of data reception by the player. The client terminal player is located at the bottom of the architecture and is the interface through which the end user interacts with the live streaming service. The player receives data from the "last-mile network," decodes it, and presents it to the user. The player's experience quality (such as first frame duration and stuttering rate) is the ultimate goal of live streaming service optimization.

[0243] In summary, the first embodiment of the data-based live streaming protocol stack architecture primarily adjusts transmission strategies through the congestion control mechanism of the kernel protocol stack to address network congestion and improve data transmission efficiency. However, due to the complete decoupling between the application layer and the kernel layer, it is difficult to make more refined strategy adjustments based on specific task requirements (such as the real-time requirements of streaming media) and the characteristics of terminal devices (such as player buffer size and hardware performance). Therefore, the above architecture has certain limitations when handling live streaming tasks with strong interactivity and diverse terminal devices.

[0244] Figure 9 is a schematic diagram of a second embodiment of a 4+7 combined live streaming protocol stack architecture according to an embodiment of this application. As shown in Figure 9, the second embodiment of the 4+7 combined live streaming protocol stack architecture evolves from the first embodiment of the data-based live streaming protocol stack architecture, realizing a more refined and intelligent network transmission and congestion control mechanism. The above changes aim to improve the user experience and transmission efficiency of real-time streaming media services. The application layer tengine-live is the entry point of the entire architecture. As a customized application layer server, tengine-live handles the request and distribution of live streaming data. tengine-live not only manages application layer protocols such as HTTP / HTTPS, but also is tightly integrated with task logic, enabling it to perceive and process specific requirements of streaming media tasks, such as video encoding information and stream characteristics. The in-band forward error correction (IBP) frame size / keyframe tolerance latency / real-time frame order components represent the close linkage between the application layer and the transport layer. IBP technology is used to improve the robustness of data transmission. Based on the characteristics of the task data monitored in real time, such as video frame size, keyframe tolerance latency, and real-time frame order, tengine-live provides guidance to the kernel protocol stack to optimize the transmission strategy. The real-time bandwidth / congestion status / network condition components reflect the kernel protocol stack's ability to monitor and analyze the real-time network status. They not only focus on traditional packet loss and latency signals, but also estimate bandwidth in real time, monitor network congestion status and overall network condition, providing a more comprehensive information foundation for adjusting transmission strategies.

[0245] Optionally, as shown in Figure 9, the kernel protocol stack is the core of network transmission, containing traditional TCP / IP protocol stack functions and congestion control algorithms based on real-time information. In the second embodiment of the above live streaming protocol stack architecture, the kernel protocol stack dynamically adjusts the transmission strategy based on the characteristics of the task data provided by the upper layer and the real-time network conditions through "bandwidth probing + transmission adaptation to stream characteristics" to ensure the smoothness and low latency of the video stream. The layer 4-7 linkage is one of the key innovations of the second embodiment of the live streaming protocol stack architecture, representing the collaborative work between the application layer (layer 4) and the transport layer (layer 7). Through the layer 4-7 linkage, tengine-live can transmit real-time information of the task data to the kernel protocol stack, which can then make more accurate bandwidth probing and transmission strategy adaptation based on this information and real-time network feedback. The network transmission stage based on task data characteristics is more intelligent, and can adjust the transmission strategy according to the characteristics of the task data (such as the frame size of the video stream, the tolerance latency of keyframes, etc.) and network conditions to achieve more efficient data transmission and a better user experience. The last-mile network component represents the last segment of the network transmission journey, namely the ISP network where the user is located, and is one of the key factors affecting the end-user experience. The "last mile network," through monitoring and analysis, provides real-time feedback on network conditions, influencing the decisions of congestion control algorithms. The client-side player is the final receiver of task data, representing the end user. Information such as the player's buffer size, hardware performance, and network conditions is fed back to the kernel protocol stack through the "last mile network," affecting the formulation of transmission strategies to achieve a good playback experience.

[0246] In summary, the key to the second embodiment of the aforementioned live streaming protocol stack architecture lies in breaking down the boundaries between the application layer and the transport layer, achieving deep integration of task data and network transmission. Through the linkage between the application layer and the kernel layer, and real-time monitoring of terminal information and network conditions, more precise and flexible adjustments to transmission strategies can be made, significantly improving the user experience of live streaming services, especially in core quality evaluation indicators such as buffering and first-frame playback latency. This architectural design reflects CDN's technological pursuit and innovation in optimizing transmission efficiency and user experience.

[0247] Figure 10 is a schematic diagram of a third embodiment of a live streaming protocol stack architecture according to an embodiment of this application. As shown in Figure 10, the third embodiment of the live streaming protocol stack architecture demonstrates how the CDN server and the client terminal player perform efficient and intelligent data transmission, with particular emphasis on the core role of 4x7 fusion in the transmission process. The application layer uses tengine-live as the application layer component, responsible for handling task logic, user requests, and data distribution. This component interacts directly with the terminal player, collecting terminal information (such as player cache status, device type, network conditions, etc.). The real-time IBP frame size / keyframe tolerance latency / real-time frame order components demonstrate tengine-live's real-time perception capability of video stream characteristics. By understanding the size of video frames, the tolerance latency of keyframes, and the frame order, it provides crucial information for transmission adaptation. The real-time bandwidth / congestion status / network condition components monitor the network bandwidth, congestion status, and overall condition in real time, providing real-time feedback for strategy decisions. Based on the collected terminal information and network characteristics, the initial strategy decision component enables the protocol stack to intelligently decide on the initial transmission strategy, including bandwidth detection mode, congestion control parameters, packet loss recovery strategies, etc., ensuring that an appropriate strategy is adopted when the connection is established. Task data-aware network transmission signifies close collaboration between the application and transport layers. The application layer directly feeds back the characteristics of task data to the transport layer to optimize data transmission. A video frame hierarchical transmission mechanism allows for hierarchical transmission based on the importance of video frames (e.g., keyframes versus non-keyframes), ensuring priority delivery of critical data. The last mile of the network is the part of network transmission closest to the end user, directly and significantly impacting user experience. Through 4x7 convergence, the protocol stack can make more precise adjustments to the network characteristics of the last mile.

[0248] Optionally, as shown in Figure 10, "personalized approach for each terminal" reflects the protocol stack's adaptability to the diversity of terminal devices. It can provide personalized transmission strategies based on the characteristics and needs of different terminals, such as mobile phones and personal computers (PCs). The real-time terminal information sensing network transmission link emphasizes the impact of information collected from the terminal in real time (such as player buffer size, hardware quality, network quality, operating system, etc.) on the network transmission strategy, making the transmission process more closely aligned with the actual situation of the terminal. The client terminal player (e.g., mobile phone, PC) receives and processes optimized transmission data from the CDN server. The interaction between the player and the server is not limited to receiving data streams but also includes real-time feedback on the terminal status, such as buffer size, device performance, and network quality. This information, in turn, influences the server's strategy adjustments. Interaction with player buffer size, hardware quality, network quality, and the terminal operating system—these parameters not only affect data reception efficiency but are also key bases for the protocol stack to adjust its transmission strategy.

[0249] In summary, the third embodiment of the live streaming protocol stack architecture described above exhibits three key characteristics: First, the protocol stack is aware of client terminal information. Through bidirectional communication between the terminal and the server, the protocol stack can obtain real-time information such as the terminal's cache status, hardware performance, and network conditions. This information directly influences congestion control strategy decisions, ensuring that the strategy adapts to the specific needs of the terminal. Second, the protocol stack possesses self-decision-making capabilities. At each request level, the protocol stack can self-determine congestion control strategies that meet the requirements based on collected terminal information and real-time network characteristics. For example, for terminals with good hardware quality and network conditions, the protocol stack will automatically adopt more aggressive strategies, such as increasing the congestion window, accelerating the pacing rate, and adjusting the slow start speed to improve transmission efficiency and user experience. Third, the protocol stack has the ability to self-correct its strategies. During connection operation, the protocol stack can periodically adjust its congestion control strategies based on real-time network feedback, possessing self-correction and optimization capabilities. For example, when packet loss or RTT anomalies are detected, it can automatically downgrade and adjust its strategies to ensure that the stability and quality of service are not affected in changing network environments.

[0250] The third embodiment of the live streaming protocol stack architecture significantly surpasses related protocol stacks by introducing end-side information and adaptive policy decisions. It can more accurately respond to complex and ever-changing network environments and terminal device characteristics, thereby providing higher-quality real-time streaming media transmission services. This architectural design fully reflects the goal of pursuing the ultimate user experience and cutting-edge technological innovation in the CDN field.

[0251] Figure 11 is a schematic diagram illustrating the design details of a third embodiment of a live streaming protocol stack architecture according to an embodiment of this application. As shown in Figure 11, the third embodiment of the live streaming protocol stack architecture demonstrates the entire data processing flow from the client player to kernel congestion control in terms of connection establishment and runtime strategy formulation and adjustment mechanisms. The request parsing phase begins with the client player sending a request to the CDN server. The request contains important information about the player, such as player parameters, operating system type, bitrate category, etc. This information is carried through the request header and enters the "application layer tengine-live parsing / aggregation" component for preliminary parsing and information classification. In the "application layer tengine-live parsing / aggregation" component, the tengine-live server performs in-depth parsing of the request information, extracts parameters related to video stream transmission, and aggregates this information for subsequent strategy formulation. The application layer bitrate information real-time perception and adjustment component is responsible for adjusting the transmission strategy based on real-time task data (such as bitrate changes, video frame type) to ensure that the transmission strategy matches the characteristics of the video stream. In the TCP connection establishment phase, the client and server begin the data transmission process. The initial strategy for the aforementioned phases is formulated by the "Initial Strategy Decision Component." Based on client announcements and data such as bitrate parsed from the application layer, the protocol stack makes strategy decisions for the initial connection phase, including initial window size and slow start characteristics. These decisions directly impact the bandwidth probing mode and data transmission speed during connection establishment.

[0252] Optionally, as shown in Figure 11, the end-edge information convergence based on network characteristics is a core feature of the third embodiment of the live streaming protocol stack architecture. It is responsible for fusing information from the application layer and the terminal side with real-time network characteristics to formulate differentiated transmission strategies. Strategy formulation is divided into "one-time policy call after connection establishment" (ONE_SHOT_MODE) and "periodic policy call during connection" (PERIOD_MODE), enabling the strategy to dynamically adjust to real-time network changes. Strategy initialization, whether in the "stable phase strategy initialization" or the "startup phase strategy initialization," initializes corresponding strategy parameters based on terminal-side information and network characteristics, such as bandwidth detection magnitude, congestion window gain, and pacing rate. These parameters are crucial for the execution of the transmission strategy. In the "kernel congestion control" component, the protocol stack implements specific congestion control algorithms, adjusting the transmission strategy in real-time based on end-edge information and network characteristics to ensure efficiency and stability during transmission. During connection runtime, the protocol stack continuously monitors network conditions through "real-time network packet loss and minimum round-trip time (minRtt) information," periodically detecting and adjusting the strategy state to cope with dynamic changes in the network environment. The policy state machine monitors the policy execution status and determines the policy's suitability based on real-time network characteristics (such as packet loss and latency changes). If the characteristics are normal, the existing policy remains unchanged; if the characteristics are abnormal, a "policy degradation" is triggered, adjusting to a more conservative policy to avoid network congestion or performance degradation. When the policy state machine detects abnormal network characteristics, it will perform policy degradation, adjusting to a more robust congestion control mode. Simultaneously, the system also has the ability to periodically update policies, ensuring that the policies maintain a good state even in changing network environments.

[0253] In summary, the third embodiment of the live streaming protocol stack architecture described above, by introducing client information and real-time network characteristics, achieves dynamic policy adjustment during the connection establishment phase and runtime. This design not only improves the intelligence of the transmission process but also enhances the adaptability and robustness of the protocol stack in the face of complex network environments. Especially in the initial connection establishment phase and when network conditions change, it can quickly make policy decisions that meet the requirements, ensuring the first frame playback quality and subsequent smoothness of the live video service. Furthermore, during policy adjustment, it possesses self-repair and optimization capabilities, significantly improving user experience and transmission efficiency. This congestion control algorithm, which deeply integrates end-side information and network characteristics, represents a significant breakthrough and innovation of the traditional congestion control mechanism in the third embodiment of the live streaming protocol stack architecture.

[0254] Figure 12 is a schematic diagram of congestion control initialization under operating system and network conditions according to an embodiment of this application. As shown in Figure 12, the "First Frame - Startup Phase Initial Strategy" section of the third embodiment of the live streaming protocol stack architecture reveals how the CDN performs refined congestion control strategy adjustments based on client information during the startup phase of the live streaming protocol stack to optimize the first frame duration and improve service quality. The client operating system identifies the type of operating system running on the terminal player. Operating system characteristics affect the client's processing power and network connection method, and are one of the foundations for determining the network type and formulating transmission strategies. The request announcement window and client minimum buffer size are used by the client to announce its buffer capacity and announcement window size through mechanisms such as request headers when establishing a connection. The size of the announcement window reflects the client's receiving capability and network conditions, while the minimum buffer size affects the player's performance in weak network environments. Initial network type determination is performed by the protocol stack based on the information announced by the client (announcement window and minimum buffer size), combined with the kernel protocol stack's perception of real-time network conditions (such as packet loss rate, latency, etc.), to make a preliminary determination of the network type. The above judgment is divided into three levels: "Good Network," "Medium Network," and "Poor Network." Different network types will trigger different policy decisions. In the internal high-aggression phase, when the network type is determined to be "Good Network," the protocol stack will adopt a more aggressive initial strategy to fully utilize network bandwidth and terminal capabilities, accelerating data transmission speed. If the network type is medium or poor, it will not directly enter the high-aggression phase to avoid excessive consumption of network resources or causing terminal buffer overflow.

[0255] Optionally, as shown in Figure 12, regarding the initial decision during the startup phase, in the early stages of connection establishment, i.e., the startup phase, the protocol stack formulates an initial transmission strategy based on the initial network type determination. The decisions made in the above phase consider not only network conditions but also the characteristics of the terminal devices, aiming to maximize transmission efficiency while ensuring stability. During the startup phase, the protocol stack's initial decision outputs a series of parameters, including the initial window size (initial congestion window size), slow start speed (rate adjustment during slow start), new features (such as dynamic parameter adjustment based on RTT), slow start bandwidth (bandwidth probing strategy in the early stages of connection establishment), and adaptation task frames (adjusting transmission priority based on frame type and size). These parameters collectively determine the data transmission strategy in the early stages of connection establishment, affecting the duration of the first frame and the efficiency of subsequent data transmission.

[0256] In summary, through the above process, the third embodiment of the live streaming protocol stack architecture can intelligently determine network conditions and dynamically adjust the congestion control strategy during connection establishment based on the specific needs of the client device. This design ensures a fast first-frame playback experience and stable data transmission under different network conditions, demonstrating the crucial role of end-edge information entropy enhancement and self-decision-making capabilities in improving user experience. Simultaneously, the self-decision-making capability enables the protocol stack to adopt appropriate strategies at the initial connection stage, avoiding resource waste or poor user experience issues that may occur under traditional fixed strategies.

[0257] Figure 13 is a schematic diagram of a network quality-based optimization decision according to an embodiment of this application. As shown in Figure 13, the "stable phase policy initialization" process in the third embodiment of the live streaming protocol stack architecture demonstrates how the decision-making process for policy initialization is performed based on the client operating system and network type, and how this decision affects the setting of specific transmission parameters. The client operating system serves as the starting point for identification and policy decision-making; client operating system information is sent to the server through mechanisms such as request headers. For initial network type determination, after receiving the client operating system information, the server combines it with other real-time network data (such as packet loss rate, latency, etc.) to determine the network type. The determination result is divided into "good network," "medium network," and "poor network," which directly affects subsequent policy initialization. After the connection enters the stable transmission phase, the protocol stack will enter different policy initialization modes based on the initial network type determination result. For a "good network," the result is "√," meaning the network conditions are excellent, and a more aggressive transmission strategy ("high-intensity strategy") can be adopted to fully utilize the high bandwidth environment. For a "medium network," the result is "○," which usually indicates that a moderate strategy is needed to ensure transmission stability and efficiency. The result for a "poor network" is also "√," meaning the network conditions are poor, and a more conservative strategy ("weak network strategy") is needed to adapt to unstable or low-bandwidth network environments.

[0258] Optionally, as shown in Figure 13, based on the strategy initialization decision during the stable phase, the output transmission parameters specifically include: Frame linkage amplitude / frequency, which determines the amplitude and frequency of video frame synchronous transmission, affecting the continuity and smoothness of the video stream; Bandwidth probe amplitude, which controls the aggressiveness of the protocol stack when probing available network bandwidth; for the "high-aggression strategy," this parameter can be set higher; Congestion window gain amplitude, which adjusts the growth rate of the congestion window size; for the "high-aggression strategy," this means faster window growth to accelerate data transmission; Pacing gain amplitude, which affects the data packet sending rate; higher pacing gain means faster data packet sending speed; Low-pass filter threshold, used to smooth changes in network conditions; a lower threshold can respond more quickly to slight changes in network conditions; minRtt sampling threshold, which defines the frequency and conditions for collecting the minimum round-trip time (minRtt); for the "high-aggression strategy," minRtt sampling can be more frequent; and a new feature, slow-start bandwidth adaptation task frame, which is used for bandwidth adaptation during the initial connection establishment phase, adjusting the slow-start strategy according to the task frame type.

[0259] In summary, the above process demonstrates the intelligence and adaptability of the third embodiment of the live streaming protocol stack architecture. It can dynamically adjust transmission strategies based on real-time network conditions and terminal device characteristics, thereby providing high-quality streaming media transmission services in various network environments. In particular, the introduction of "high-intensity strategy," "moderate strategy (backup)," and "weak network strategy" not only improves transmission efficiency under good network conditions but also ensures transmission stability and user experience under medium and poor network conditions, achieving broad adaptability and optimization of streaming media services. The strategy initialization based on network type and terminal information is a key step in improving the quality and efficiency of live streaming services, fully demonstrating the innovation and fine-grained control capabilities of the third embodiment of the live streaming protocol stack architecture in congestion control algorithms.

[0260] Figure 14 is a schematic diagram of a runtime policy self-optimization and degradation mechanism according to an embodiment of this application. As shown in Figure 14, in the "online update and runtime policy self-adjustment" process of the third embodiment of the live streaming protocol stack architecture, the importance of dynamically adjusting the policy is emphasized to ensure that network communication can be continuously optimized in a changing environment. Online updates and runtime adjustments: After the connection is established, the protocol stack will continuously perform online updates and runtime adjustments to adapt to real-time changes in network conditions and ensure the appropriate effect of the transmission policy. Policy status detection and network information updates: The protocol stack will periodically collect information from two main sources for status detection and policy adjustment—application-side / end-side information updates and real-time network packet loss and minRtt information updates. The former provides updates on terminal information such as player cache, device performance, and operating system, while the latter provides real-time feedback on network packet loss rate and minimum round-trip time (minRtt). This information is crucial for determining network conditions and adjusting transmission policies. Determining multi-round packet loss and minRtt conditions is the core of policy adjustment decisions. Based on the collected multi-round packet loss rate and minRtt information, it can be determined whether the network exhibits abnormal characteristics. The above-mentioned judgment process is carried out periodically, ensuring the timeliness and accuracy of strategy adjustments.

[0261] Optionally, as shown in Figure 14, regarding policy maintenance and policy degradation, based on the judgment results of multiple rounds of packet loss and minRtt, the protocol stack will adopt one of two strategies: "policy maintenance" or "policy degradation". If the network characteristics are judged to be normal, that is, the packet loss and minRtt changes are within the allowable range, the protocol stack will maintain the current transmission policy unchanged to maintain stable and efficient data transmission. "Policy degradation" occurs in scenarios where the network characteristics are judged to be abnormal, such as an increase in packet loss rate or a significant increase in minRtt. This usually means network congestion or other problems. At this time, the protocol stack will automatically adjust to a more conservative policy to avoid further transmission problems and protect the user experience. The periodic network information update & policy status detection component is connected to the central judgment mechanism by arrows, indicating that the protocol stack will periodically update network information and detect policy status, ensuring the real-time nature and effectiveness of the judgment process and policy adjustment. The application-side / end-side information update and real-time network packet loss and minRtt information are pointed to the central judgment mechanism by arrows, demonstrating the role of end-side information and network data in policy adjustment. Real-time updates of end-side information enable the protocol stack to understand the status of terminal devices more accurately, while real-time network data provides immediate feedback on network conditions. The two work together to influence policy decisions, achieving the integration and utilization of end-side information.

[0262] In summary, the entire "online update" process demonstrates the runtime sensitivity and adaptability of the third embodiment of the live streaming protocol stack architecture to network characteristics. It can automatically adjust and optimize transmission strategies based on real-time network conditions and terminal information to address network issues such as packet loss and latency, ensuring service quality. This periodic self-regulation mechanism is a significant innovation in the congestion control algorithm of this architecture, effectively improving the user experience and transmission efficiency of live streaming services, especially under unstable or highly variable network conditions.

[0263] In this embodiment, if streaming media data transmission is required, the target request from the playback client can be monitored in real time. If a target request for obtaining streaming media data in a playback service scenario is detected on the playback client, the attribute information set of the playback client can be obtained from the target request. A target transmission strategy matching the attribute information set and network characteristics can be determined from the transmission strategy set. The streaming media data can then be transmitted to the playback client for playback according to the target transmission strategy. In this embodiment, by monitoring the target request of the terminal device in real time, obtaining terminal attribute information, and combining network characteristics for dynamic strategy decision-making and self-adjustment, a comprehensive optimization of the streaming media data service quality is achieved, effectively improving the transmission quality of streaming media data. Through the aforementioned end-edge fusion strategy in real-time, highly interactive task scenarios, the user experience can be significantly improved and the service competitiveness enhanced, thereby achieving the technical effect of improving the transmission quality of streaming media data and solving the technical problem of poor streaming media data transmission quality.

[0264] According to an embodiment of this application, a streaming media data transmission apparatus is also provided for implementing the streaming media data transmission method shown in FIG2 above.

[0265] Figure 15 is a schematic diagram of a streaming media data transmission device according to an embodiment of the present application. As shown in Figure 15, the streaming media data transmission device 1500 may include: a first acquisition component 1502, a first determination component 1504 and a first transmission component 1508.

[0266] The first acquisition component 1502 is used to obtain the attribute information set of the playback client from the target request in response to the target request from the playback client.

[0267] The first determining component 1504 is used to determine, within the transmission strategy set, a target transmission strategy that matches the attribute information set and the network characteristics of the network.

[0268] The first transmission component 1508 is used to transmit streaming media data to the playback client.

[0269] Here, the first acquisition component 1502, the first determination component 1504, and the first transmission component 1508 correspond to steps S202 to S206 in the above embodiments. The three components and the corresponding steps implement the same instances and application scenarios, but are not limited to the content disclosed in the above embodiments. It should be noted that the above components may be hardware or software components stored in memory (e.g., memory 1804) and processed by one or more processors (e.g., processors 1802a, 1802b, ..., 1802n). The above components may also be part of a device and run in the computer terminal A provided in the following embodiment.

[0270] According to an embodiment of this application, a streaming media data transmission apparatus is also provided for implementing the streaming media data transmission method shown in FIG3 above.

[0271] Figure 16 is a schematic diagram of a streaming media data transmission device according to an embodiment of the present application. As shown in Figure 16, the streaming media data transmission device 1600 may include: a parsing component 1602, a second determining component 1604, and a second transmission component 1606.

[0272] Parsing component 1602 is used to parse the attribute information set of the playback client from the target request in response to the target request from the playback client.

[0273] The second determining component 1604 is used to determine, within the transmission strategy set, a target transmission strategy that matches the attribute information set and the network characteristics of the network.

[0274] The second transmission component 1606 is used to transmit streaming media data to the playback client according to the target transmission strategy.

[0275] It should be noted that the parsing component 1602, the second determining component 1604, and the second transmission component 1606 mentioned above correspond to steps S302 to S306 in the above embodiments. The three components and their corresponding steps implement the same instances and application scenarios, but are not limited to the content disclosed in the above embodiments. It should be noted that the above components may be hardware or software components stored in memory (e.g., memory 1804) and processed by one or more processors (e.g., processors 1802a, 1802b, ..., 1802n). The above components may also be part of a device and run in the computer terminal A provided in the following embodiments.

[0276] According to an embodiment of this application, a streaming media data transmission apparatus is also provided for implementing the streaming media data transmission method shown in FIG4 above.

[0277] Figure 17 is a schematic diagram of a streaming media data transmission device according to an embodiment of the present application. As shown in Figure 17, the streaming media data transmission device 1700 may include: a first invocation component 1702, a second acquisition component 1704, a third determination component 1706, and a second invocation component 1708.

[0278] The first calling component 1702 is used to obtain the target request from the playback client by calling the first interface.

[0279] The second acquisition component 1704 is used to respond to the target request and obtain the attribute information set of the playback client from the target request.

[0280] The third determining component 1706 is used to determine, within the transmission strategy set, a target transmission strategy that matches the attribute information set and the network characteristics of the network.

[0281] The second calling component 1708 is used to transmit streaming media data to the playback client by calling the second interface, according to the target transmission strategy.

[0282] Here, the first invocation component 1702, the second acquisition component 1704, the third determination component 1706, and the second invocation component 1708 correspond to steps S402 to S408 in the above embodiments. The four components and the corresponding steps implement the same instances and application scenarios, but are not limited to the content disclosed in the above embodiments. It should be noted that the above components can be hardware or software components stored in memory (e.g., memory 1804) and processed by one or more processors (e.g., processors 1802a, 1802b, ..., 1802n). The above components can also be part of a device and run in the computer terminal A provided in the following embodiment.

[0283] In this streaming media data transmission device, if streaming media data needs to be transmitted, the target request of the playback client can be monitored in real time. If a target request for streaming media data in a playback service scenario is detected on the playback client, the attribute information set of the playback client can be obtained from the target request. A target transmission strategy matching the attribute information set and network characteristics can be determined from the transmission strategy set. The streaming media data can be transmitted to the playback client for playback according to the target transmission strategy. In this embodiment, by monitoring the target request of the terminal device in real time, obtaining terminal attribute information, and combining network characteristics for dynamic strategy decision-making and self-adjustment, a comprehensive optimization of the streaming media data service quality is ultimately achieved, effectively improving the transmission quality of streaming media data. Through the above-mentioned end-edge fusion strategy in real-time, highly interactive task scenarios, the user experience can be significantly improved and the service competitiveness enhanced, thereby achieving the technical effect of improving the transmission quality of streaming media data and solving the technical problem of poor transmission quality of streaming media data.

[0284] Embodiments of this application may provide a computer terminal, which may be any computer terminal device in a group of computer terminals. Optionally, in this embodiment, the aforementioned computer terminal may also be replaced by a mobile terminal or other terminal device.

[0285] Optionally, in this embodiment, the computer terminal may be located in at least one of a plurality of network devices in a computer network.

[0286] In this embodiment, the computer terminal described above can execute the program code for the steps in the streaming media data transmission method.

[0287] Optionally, FIG18 is a structural block diagram of a computer terminal according to an embodiment of the present application. As shown in FIG18, the computer terminal A may include: one or more (only one is shown in the figure) processors 1802, memory 1804, and transmission devices 1806.

[0288] The memory can be used to store software programs and components, such as the program instructions / components corresponding to the streaming media data transmission method and apparatus in this application embodiment. The processor executes various functional applications and data processing by running the software programs and components stored in the memory, thereby realizing the aforementioned streaming media data transmission method. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely located relative to the processor, which can be connected to computer terminal A via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0289] This application provides a method for transmitting streaming media data. In this embodiment, if network resources need to be loaded, it is possible to monitor in real time whether a client has a request to access a certain page (i.e., a front-end page), i.e., a page access request. If a page access request is detected, it can be analyzed to determine whether the client has the necessary permissions to access the CDN. If the access request does not contain the aforementioned identification information, the network resources on that page cannot be loaded; otherwise, at least one accessible target page matching the permissions in the identification information can be identified. This target page can then be returned to the corresponding client, and the network resources on that target page can be loaded on the client. This embodiment proposes a front-end resource adaptive loading method, the core of which is to automatically determine whether the client has the necessary permissions to access CDN resources based on the identification information contained in the page access request, and return an appropriate page version accordingly, thereby achieving efficient and intelligent resource loading without manual intervention. The above method maintains good adaptability and performance in both public and private cloud environments, thus achieving the technical effect of improving the transmission quality of streaming media data and solving the technical problem of poor transmission quality of streaming media data.

[0290] It will be understood by those skilled in the art that the structure shown in Figure 18 is merely illustrative, and computer terminal A can also be a smartphone, such as an Android phone, an iOS phone, a tablet computer, a PDA, a mobile internet device (MID), a personal digital assistant (PAD), or other terminal devices. Figure 18 does not limit the structure of the aforementioned computer terminal A. For example, computer terminal A may include more or fewer components (such as network interfaces, display devices, etc.) than shown in Figure 18, or have a different configuration than that shown in Figure 18.

[0291] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0292] Embodiments of this application also provide a computer-readable storage medium. Optionally, in this embodiment, the computer-readable storage medium can be used to store the program code executed by the streaming media data transmission method provided in Embodiment 1.

[0293] Optionally, in this embodiment, the computer-readable storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0294] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the steps in the above-described method for loading web page resources.

[0295] Embodiments of this application may provide an electronic device that may include a memory and a processor.

[0296] Figure 19 is a block diagram of an electronic device for a streaming media data transmission method according to an embodiment of this application. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present application described and / or claimed herein.

[0297] As shown in Figure 19, device 1900 includes a computing component 1901, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 1902 or a computer program loaded into random access memory (RAM) 1903 from storage component 1908. RAM 1903 may also store various programs and data required for the operation of device 1900. The computing component 1901, ROM 1902, and RAM 1903 are interconnected via bus 1904. Input / output (I / O) interface 1905 is also connected to bus 1904.

[0298] Multiple components in device 1900 are connected to I / O interface 1905, including: input components 1906, such as a keyboard, mouse, etc.; output components 1904, such as various types of displays, speakers, etc.; storage components 1908, such as disks, optical disks, etc.; and communication components 1909, such as network interface cards, modems, wireless transceivers, etc. Communication component 1909 allows device 1900 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0299] Computing component 1901 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of computing component 1901 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing components running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Computing component 1901 performs the various methods and processes described above, such as methods for transmitting streaming media data. For example, in some embodiments, the method for transmitting streaming media data may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage component 1908. In some embodiments, part or all of the computer program may be loaded and / or installed on device 1900 via ROM 1902 and / or communication component 1909. When the computer program is loaded into RAM 1903 and executed by computing component 1901, one or more steps of the method for transmitting streaming media data described above may be performed. Alternatively, in other embodiments, computing component 1901 may be configured to perform a method for transmitting streaming media data by any other suitable means (e.g., by means of firmware).

[0300] Embodiments of this application also provide a computer program product. Optionally, in this embodiment, the computer program product may include a computer program that, when executed by a processor, implements the streaming media data transmission method of the embodiments of this application.

[0301] According to an embodiment of this application, a method for transmitting streaming media data is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0302] The method embodiments provided in this application can be executed in a mobile terminal, computer terminal, or similar computing device. Figure 20 is a hardware structure block diagram of a computer terminal (or mobile device) for implementing a streaming media data transmission method according to an embodiment of this application. As shown in Figure 20, the computer terminal 200 (or mobile device) may include one or more processors 2002 (shown as 2002a, 2002b, ..., 2002n in the figure) (processor 2002 may include, but is not limited to, a microprocessor (MicroController Unit, abbreviated as MCU) or a programmable gate array (FPGA), etc.), a memory 2004 for storing data, and a transmission device 2006 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a Universal Serial Bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that the structure shown in Figure 20 is only illustrative and does not limit the structure of the above-described electronic device. For example, computer terminal 200 may also include more or fewer components than shown in FIG. 20, or have a different configuration than shown in FIG. 20.

[0303] The hardware structure block diagram shown in Figure 20 can serve as an exemplary block diagram not only for the computer terminal 200 (or mobile device) described above, but also as an exemplary block diagram for the server described above. In one optional embodiment, Figure 20 illustrates an example of using the computer terminal 200 (or mobile device) shown in Figure 20 as a computing node in the computing environment 2001.

[0304] Figure 21 is a structural block diagram of a computing environment for a streaming media data transmission method according to an embodiment of this application. As shown in Figure 21, the computing environment 2101 includes multiple computing nodes (such as servers) running on a distributed network (shown as 2110-1, 2110-2, ... in the figure). Each computing node contains local processing and memory resources, and the end user 2102 can remotely run applications or store data in the computing environment 2101. The applications can be provided as multiple services 2120-1, 2120-2, 2120-3, and 2120-4 in the computing environment 2101, representing services "F", "G", "I", and "H", respectively.

[0305] End user 2102 can provide and access services through a web browser or other software application on the client. In some embodiments, the provisioning and / or requests of end user 2102 can be provided to ingress gateway 2130. Ingress gateway 2130 may include a corresponding agent to handle the provisioning and / or requests for services (one or more services provided in computing environment 2101).

[0306] The service is provided or deployed based on various virtualization technologies supported by the computing environment 2101. In some embodiments, the service may be provided based on virtual machine (VM)-based virtualization, container-based virtualization, and / or similar methods. Virtual machine-based virtualization can simulate a real computer by initializing a virtual machine, executing programs and applications without directly accessing any actual hardware resources. While the machine is virtualized by a virtual machine, container-based virtualization can launch containers to virtualize an entire operating system so that multiple workloads can run on a single instance of the operating system.

[0307] In one embodiment based on container virtualization, several containers of a service can be assembled into a Pod (e.g., a Kubernetes Pod). For example, as shown in Figure 21, service 2120-2 can be equipped with one or more Pods 2140-1, 2140-2, ..., 2140-N (collectively referred to as Pods). A Pod can include a proxy 2145 and one or more containers 2142-1, 2142-2, ..., 2142-M (collectively referred to as containers). One or more containers in a Pod handle requests related to one or more corresponding functions of the service. The proxy 2145 typically controls service-related network functions such as routing and load balancing. Other services can also be equipped with Pods similar to Pods.

[0308] During operation, executing a user request from end user 2102 may require calling one or more services in computing environment 2101, and executing one or more functions of one service may require calling one or more functions of another service. As shown in Figure 21, service "F" 2120-1 receives the user request from end user 2102 from ingress gateway 2130. Service "F" 2120-1 can call service "G" 120-2, and service "G" 2120-2 can request service "I" 2120-3 to execute one or more functions.

[0309] The aforementioned computing environment can be a cloud computing environment, where resource allocation is managed by cloud services, allowing functionality development without the need for analysis, implementation, tuning, or scaling of servers. This computing environment allows developers to execute event-responsive code without building or maintaining complex infrastructure. Services can be partitioned into a set of functions that can automatically and independently scale, rather than scaling a single hardware device to handle potential loads.

[0310] The various embodiments of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard parts (ASSPs), system-on-a-chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0311] The program code used to implement the methods of this application may be written in any combination of one or more programming languages. The program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0312] In the context of this application, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0313] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a cathode ray tube (CRT) or liquid crystal display (LCD)) for displaying information to the user; a monitor; and a keyboard and pointing device (e.g., a mouse or pathball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0314] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication (e.g., a communication network) of any form or medium. Examples of communication networks include Local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

[0315] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0316] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0317] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0318] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of components is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple components or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interface; the indirect coupling or communication connection of components or components may be electrical or other forms.

[0319] The components described as separate parts may or may not be physically separate. The components shown as components may or may not be physical components; that is, they may be located in one place or distributed across multiple network components. Some or all of the components can be selected to achieve the purpose of this embodiment according to actual needs.

[0320] Furthermore, the functional components in the various embodiments of this application can be integrated into one processing component, or each component can exist physically separately, or two or more components can be integrated into one component. The integrated components described above can be implemented in hardware or as software functional components.

[0321] If integrated components are implemented as software functional components and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0322] The above are merely preferred embodiments of this application. It should be noted that those skilled in the art can make several improvements and modifications without departing from the principles of this application, and such improvements and modifications should also be considered within the scope of protection of this application. Industrial applicability

[0323] In this embodiment, if streaming media data transmission is required, the target request of the playback client can be monitored in real time. If a target request for streaming media data in a scenario requiring playback service is detected on the playback client, the attribute information set of the playback client can be obtained from the target request. A target transmission strategy matching the attribute information set and network characteristics can be determined from the transmission strategy set. The streaming media data can be transmitted to the playback client for playback according to the target transmission strategy. In this embodiment, by monitoring the target request of the terminal device in real time, obtaining terminal attribute information, and combining network characteristics for dynamic strategy decision-making and self-adjustment, a comprehensive adjustment of the streaming media data service quality is ultimately achieved, effectively improving the transmission quality of streaming media data. Through the above-mentioned end-edge fusion strategy in real-time, highly interactive task scenarios, the user experience can be significantly improved and the service competitiveness enhanced, thereby achieving the technical effect of improving the transmission quality of streaming media data and solving the technical problem of poor transmission quality of streaming media data.

Claims

1. A method for transmitting streaming media data, applied to a server running a protocol stack, the server being used to transmit data with a playback client over a network, and executing the following methods through the protocol stack: In response to a target request from the playback client, a set of attribute information of the playback client is obtained from the target request, wherein, The target request is used to request streaming media data in the context of a playback service. The attribute information set includes at least one attribute information of the playback client during operation, and / or at least one attribute information of the terminal device on which the playback client is installed during operation. In the transmission strategy set, a target transmission strategy that matches the attribute information set and the network characteristics of the network is determined, wherein the transmission strategies in the transmission strategy set are used to represent the rules by which the server transmits data in the playback service scenario, and the network characteristics are used to represent the attributes of the network during operation; According to the target transmission strategy, the streaming media data is transmitted to the playback client, wherein the streaming media data is used for playback on the playback client.

2. The method according to claim 1, wherein, The quality index achieved by transmitting the streaming media data according to the target transmission strategy is higher than the quality index achieved by transmitting the streaming media data according to transmission strategies other than the target transmission strategy in the transmission strategy set.

3. The method according to claim 2, wherein, The method further includes: From the perspective of target comparison, the quality indicators achieved by transmitting the streaming media data according to the target transmission strategy are compared with the quality indicators achieved by transmitting the streaming media data according to transmission strategies other than the target transmission strategy in the transmission strategy set. The target comparison dimension includes at least one of the following: first frame playback delay, number of stutters, video smoothness, and audio synchronization.

4. The method according to claim 1, wherein, In the transmission policy set, determining a target transmission policy that matches the attribute information set and the network characteristics includes: Determine a policy level that matches the attribute information set and the network characteristics, wherein the policy level is used to represent the stringency required by the server for the data transmission process in the playback service scenario; In the transmission policy set, the target transmission policy that satisfies the policy level is determined.

5. The method according to claim 4, wherein, The method further includes: Determine the playback stage of the streaming media data played by the playback client; Determining a policy level that matches the attribute information set and the network features includes: determining a policy level that matches the attribute information set under the playback stage and the network features under the playback stage, wherein different playback stages correspond to different attribute information sets.

6. The method according to claim 5, wherein, In the transmission policy set, determining the target transmission policy that satisfies the policy level includes: During the playback phase, at least a set of transmission parameters associated with the policy level is determined, wherein different playback phases correspond to different sets of transmission parameters, and the transmission parameters in the set of transmission parameters are used to represent the parameters required to transmit the streaming media data under the policy level. In the transmission strategy set, the transmission strategy that includes at least the transmission parameter set is determined as the target transmission strategy.

7. The method according to claim 6, wherein, During the playback phase, at least the set of transmission parameters associated with the policy level is determined, including: During the playback phase, parameter adjustment information associated with the strategy level and the transmission parameter set are determined, wherein the parameter adjustment information is used to represent the rules for adjusting the transmission parameters in the transmission parameter set under the strategy level; In the transmission strategy set, a transmission strategy that includes at least the transmission parameter set is determined as the target transmission strategy, including: in the transmission strategy set, a transmission strategy that includes the parameter adjustment information and the transmission parameter set is determined as the target transmission strategy.

8. The method according to claim 6, wherein, The streaming media data includes a first frame, and the playback phase includes a start playback phase for playing the first frame. During the playback phase, at least a set of transmission parameters associated with the policy level is determined, including: During the playback startup phase, at least one set of transmission parameters associated with the strategy level is determined, wherein the set of transmission parameters includes at least one of the following transmission parameters: the first window size corresponding to the first frame, the startup speed of the first frame, and the startup characteristic parameters of the first frame, wherein the duration required to transmit the first frame according to the transmission parameters in the set of transmission parameters is shorter than the duration required to transmit the streaming media data according to the transmission strategy set other than the target transmission strategy.

9. The method according to claim 6, wherein, The playback phase includes a stable playback phase of the streaming media data. During this playback phase, at least a set of transmission parameters associated with the policy level is determined, including: During the stable playback phase, at least one set of transmission parameters associated with the strategy level is determined, wherein the set of transmission parameters includes at least one of the following transmission parameters: frame linkage parameters, bandwidth detection parameters, window control parameters, transmission rate control parameters, filtering threshold, and sampling parameters. The stuttering performance achieved by transmitting the streaming media data according to the transmission parameters in the set of transmission parameters is lower than the stuttering performance achieved by transmitting the streaming media data according to transmission strategies other than the target transmission strategy in the set of transmission strategies.

10. The method according to claim 5, wherein, In response to a request from the playback client, the attribute information set of the playback client is obtained from the target request, including: In response to the target request from the playback client during the playback phase, the attribute information set of the playback client during the playback phase is obtained from the target request.

11. The method according to claim 10, wherein, The streaming media data includes the first frame, and the playback stage includes the start playback stage of the first frame. In response to the target request from the playback client during the playback stage, the attribute information set of the playback client during the playback stage is obtained from the target request, including: In response to the target request sent by the playback client during the playback startup phase, the attribute information set of the playback client during the playback startup phase is obtained from the target request, wherein the attribute information set includes at least one of the following: the type of operating system of the terminal device, the maximum amount of data that the playback client is allowed to receive, the minimum buffer size of the playback client, and the quality index of the terminal device.

12. The method according to claim 10, wherein, The playback phase includes a stable playback phase of the streaming media data. In response to a target request from the playback client during the playback phase, the attribute information set of the playback client during the playback phase is obtained from the target request, including: In response to the target request sent by the playback client during the stable playback phase, the attribute information set of the playback client during the stable playback phase is obtained from the target request, wherein the attribute information set includes at least one of the following: the type of operating system of the terminal device, the minimum cache size of the playback client, the bitrate category of the playback client, and the quality index of the terminal device.

13. The method according to any one of claims 1 to 12, wherein, In the process of transmitting the streaming media data to the playback client according to the target transmission strategy, the method further includes: Obtain the update results of the attribute information set and / or the network features; Determine the degree of matching between the update result and the target transmission strategy; Based on the matching degree, an update strategy for the target transmission strategy is determined, wherein the update strategy is used to represent the rule for whether to update the target transmission strategy.

14. The method according to claim 13, wherein, Based on the matching degree, an update strategy for the target transmission strategy is determined, including: In response to the matching degree being less than the matching degree threshold, the update policy is determined to be a rule that allows updating the target transmission policy; In response to the matching degree being greater than or equal to the matching degree threshold, the update strategy is determined to be a rule that maintains the target transmission strategy.

15. The method according to claim 14, wherein, In response to the matching degree being less than a matching degree threshold, determining the update policy as a rule that allows updating the target transmission policy includes: In response to the matching degree being less than the matching degree threshold, the update policy is determined to be a rule that allows the policy level of the target transmission policy to be updated.

16. A method for transmitting streaming media data, applied to a content delivery network node running a live streaming protocol stack, the content delivery network node being used to transmit data with a playback client over a network, and executing the following method through the live streaming protocol stack: In response to a target request from the playback client, the attribute information set of the playback client is parsed from the target request, wherein, The target request is used to request the transmission of streaming media data in a live streaming service scenario. The attribute information set includes at least one attribute information of the playback client during operation, and / or at least one attribute information of the terminal device on which the playback client is installed during operation. In the transmission strategy set, a target transmission strategy that matches the attribute information set and the network characteristics of the network is determined, wherein the transmission strategies in the transmission strategy set are used to represent the rules for the content distribution network nodes to transmit data in the live streaming service scenario, and the network characteristics are used to represent the attributes of the network during operation; According to the target transmission strategy, the streaming media data is transmitted to the playback client, wherein the streaming media data is used for playback on the playback client.

17. A method for transmitting streaming media data, applied to a server running a protocol stack, the server being used to transmit data with a playback client over a network, and executing the following methods through the protocol stack: The target request from the playback client is obtained by calling the first interface, wherein... The first interface includes a first parameter, the value of which is the target request; In response to the target request, the attribute information set of the playback client is obtained from the target request, wherein the target request is used to request the transmission of streaming media data in the playback service scenario, and the attribute information set includes at least one attribute information of the playback client during operation, and / or at least one attribute information of the terminal device on which the playback client is installed during operation; In the transmission strategy set, a target transmission strategy that matches the attribute information set and the network characteristics of the network is determined, wherein the transmission strategies in the transmission strategy set are used to represent the rules by which the server transmits data in the playback service scenario, and the network characteristics are used to represent the attributes of the network during operation; By calling the second interface, the streaming media data is transmitted to the playback client according to the target transmission strategy. The second interface includes a second parameter, the value of which is the target transmission strategy. The streaming media data is used for playback on the playback client.

18. A streaming media data transmission system, comprising: A server and a playback client running a protocol stack are provided. The server is used to transmit data with the playback client over a network. The playback client is used to send a target request, wherein the target request is used to request the transmission of streaming media data in the playback service scenario; The server is configured to respond to the target request via the protocol stack, and obtain an attribute information set of the playback client from the target request, wherein the attribute information set includes at least one attribute information of the playback client during operation, and / or at least one attribute information of the terminal device on which the playback client is installed during operation; and determine a target transmission strategy in a transmission strategy set that matches the attribute information set and the network characteristics of the network, wherein the transmission strategies in the transmission strategy set are used to represent the rules by which the server performs data transmission in the playback service scenario, and the network characteristics are used to represent the attributes of the network during operation; According to the target transmission strategy, the streaming media data is transmitted to the playback client; The playback client is used to play the streaming media data.

19. An electronic device comprising: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, performs the method according to any one of claims 1 to 17.

20. A computer-readable storage medium comprising a stored executable program, wherein, When the executable program is executed, it controls the device containing the storage medium to perform the method described in any one of claims 1 to 17.

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