Optimization method and device for streaming media service, electronic equipment and storage medium

Abstract

The invention relates to the technical field of wireless communication, and particularly provides an optimization method and device for a streaming media service, electronic equipment and a storage medium. The optimization method for the streaming media service comprises the following steps: acquiring service information of the streaming media service; generating prediction information of the experience quality of the streaming media service based on the service information of the streaming media service; and generating optimization information for optimizing the quality of experience of the streaming media service at least based on the prediction information, wherein the prediction information is at least used for indicating the time information of the predicted experience quality change. According to the optimization method for the streaming media service, the influence of behaviors of all participants of the streaming media service on the quality experience of the streaming media service is considered at the same time. Besides, before the quality experience of the streaming media service deteriorates, an optimization strategy of the quality experience is predicted and determined in advance, so that large fluctuation of the quality experience is effectively avoided, and good user experience is guaranteed.

Description

Technical Field

[0001] The present disclosure relates to the field of mobile communication technology, and in particular to an optimization method, device, electronic device and storage medium for streaming media services. Background Art

[0002] In the field of mobile communication technology, for streaming media services such as HTTP Dynamic Adaptive Streaming (DASH) and Virtual Reality (VR), Quality of Experience (QoE) is usually used to characterize the user's perception of the quality and performance of the service, where some indicators of Quality of Experience (QoE) can be presented through measurement indicators (such as delay time, buffering time, damage duration, etc.).

[0003] Currently, artificial intelligence / machine learning (AI / ML) technology has been introduced into the field of mobile communications. In application scenarios such as network energy saving, network load balancing and mobility optimization, it analyzes the collected data and automatically processes the output predictions or response strategies to improve network management and user experience. However, the current AI models usually only focus on the service application layer in the quality of experience (QoE) optimization scenario, lack of consideration for all parties involved in the service, and it is difficult to perform effective quality of experience (QoE) prediction and optimization for specific services such as streaming services. Summary of the invention

[0004] The present disclosure is proposed in view of the above problems. The present disclosure provides an optimization method, device, electronic device and storage medium for streaming media services.

[0005] According to one aspect of the present disclosure, there is provided an optimization method for streaming media services, characterized in that it includes: acquiring service information of the streaming media services; generating prediction information of the experience quality of the streaming media services based on the service information of the streaming media services; and generating optimization information for optimizing the experience quality of the streaming media services based at least on the prediction information; wherein the prediction information is at least used to indicate time information of the predicted change in the experience quality.

[0006] In addition, according to one aspect of the present disclosure, an optimization method for streaming media services is provided, wherein the service information includes at least one of the following: buffer information for the streaming media service, delay information of the streaming media service, bit rate information of the streaming media service, time information related to changes in the quality of experience, location information related to changes in the quality of experience, and quality information of a wireless channel used by the streaming media service.

[0007] In addition, according to an optimization method for streaming media services in one aspect of the present disclosure, the prediction information includes at least one of the following: parameter information causing the change in the quality of experience, and time information of the change in the quality of experience.

[0008] In addition, the optimization method for streaming media services according to one aspect of the present disclosure further includes: sending the optimization information to one or more of the user equipment, a base station for network access of the user equipment, and a server device providing the streaming media service.

[0009] In addition, according to an optimization method for streaming media services according to one aspect of the present disclosure, the optimization information is used to indicate at least one of the following: target bit rate information of the streaming media service, target parameter information of a buffer used for the streaming media service, target delay parameter information of the streaming media service, target time-frequency resource parameter information of the user equipment, indication information of the server sending the target bit rate streaming media, and optimization time information of the user equipment, the base station and / or the server.

[0010] According to another aspect of the present disclosure, an optimization device for streaming media services is provided, comprising: a service information acquisition unit, configured to acquire service information of the streaming media service; a prediction information generation unit, configured to generate prediction information of the experience quality of the streaming media service based on the service information of the streaming media service; and an optimization information generation unit, configured to generate optimization information for optimizing the experience quality of the streaming media service based at least on the prediction information; wherein the prediction information is at least used to indicate time information of the predicted change in the experience quality.

[0011] In addition, according to another aspect of the present disclosure, an optimization device for streaming media services is provided, wherein the service information includes at least one of the following: cache information for the streaming media service, delay information of the streaming media service, bit rate information of the streaming media service, time information related to changes in the quality of experience, location information related to changes in the quality of experience, and quality information of a wireless channel used by the streaming media service.

[0012] In addition, according to another aspect of the present disclosure, in the optimization device for streaming media services, the prediction information includes at least one of the following: parameter information causing the change in the quality of experience, and time information of the change in the quality of experience.

[0013] In addition, according to another aspect of the present disclosure, the optimization device for streaming media services also includes: an optimization information sending unit, configured to send the optimization information to one or more of the user equipment, a base station for network access of the user equipment, and a server device providing the streaming media service.

[0014] In addition, according to another aspect of the present disclosure, an optimization device for streaming media services is provided, wherein the optimization information is used to indicate at least one of the following: target bit rate information of the streaming media service, target parameter information of a buffer used for the streaming media service, target delay parameter information of the streaming media service, target time-frequency resource parameter information of the user equipment, indication information of the server sending the target bit rate streaming media, and optimization time information of the user equipment, the base station and / or the server.

[0015] According to another aspect of the present disclosure, an electronic device is provided, including: a memory for storing computer-readable instructions; and a processor for executing the computer-readable instructions so that the electronic device executes the optimization method for streaming media services as described above.

[0016] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium is provided for storing computer-readable instructions. When the computer-readable instructions are executed by a processor, the processor executes the optimization method for streaming media services as described above.

[0017] As will be described in detail below, according to the embodiments of the present disclosure, there are optimization methods, devices, electronic devices, and storage media for streaming media services. According to the embodiments of the present disclosure, the optimization method for streaming media services simultaneously considers the impact of the behaviors of various participants in the streaming media services on the quality experience of the streaming media services, rather than just taking the application layer as the optimization object of the streaming media services. In addition, according to the embodiments of the present disclosure, the optimization method for streaming media services predicts and determines the optimization strategy of the quality experience in advance before the quality experience of the streaming media services deteriorates, thereby effectively avoiding large fluctuations in the quality experience and ensuring a good user experience.

[0018] It is to be understood that both the foregoing general description and the following detailed description are exemplary, and are intended to provide further explanation of the technology as claimed. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The above and other purposes, features and advantages of the present disclosure will become more apparent by describing the embodiments of the present disclosure in more detail in conjunction with the accompanying drawings. The accompanying drawings are used to provide a further understanding of the embodiments of the present disclosure and constitute a part of the specification. Together with the embodiments of the present disclosure, they are used to explain the present disclosure and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0020] Figure 1 It is a schematic diagram of a scenario illustrating the application of the optimization method for streaming media services according to an embodiment of the present disclosure.

[0021] Figure 2 It is a schematic diagram illustrating an AI model for implementing an optimization method for streaming media services according to an embodiment of the present disclosure.

[0022] Figure 3 is a flowchart illustrating an optimization method for streaming media services according to an embodiment of the present disclosure.

[0023] Figure 4 and Figure 5 is a flowchart further illustrating an optimization method for streaming media services according to an embodiment of the present disclosure.

[0024] Figure 6 is a block diagram illustrating an optimization device for streaming media services according to an embodiment of the present disclosure.

[0025] Figure 7 is a hardware block diagram illustrating an electronic device according to an embodiment of the present disclosure.

[0026] Figure 8 is a schematic diagram illustrating a computer-readable storage medium according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solution and advantages of the present disclosure more obvious, the exemplary embodiments according to the present disclosure will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all the embodiments of the present disclosure, and it should be understood that the present disclosure is not limited to the exemplary embodiments described here.

[0028] First, refer to Figure 1 A scenario for applying the optimization method for streaming media services according to an embodiment of the present disclosure is described.

[0029] like Figure 1 As shown, the user equipment 10 is connected to the access network base station 20 in communication, and is connected to the core network server 30 via the base station 20. Through the server 30 and the base station 20, the user equipment 10 is provided with streaming services such as HTTP dynamic adaptive streaming (DASH) and virtual reality (VR).

[0030] The optimization method for streaming media services according to the embodiment of the present disclosure can be used, for example, for Figure 1In the scenario shown. More specifically, the optimization method for streaming media services according to an embodiment of the present disclosure can be performed, for example, by an AI model. For streaming media services, training, testing, and updating of the AI ​​model can be performed based on business information. The trained AI model can further perform a prediction of the quality of experience of the streaming media service based on the business information. As will be described in detail below, the prediction of the quality of experience of the streaming media service may include a prediction result about the quality of experience and decision information for optimizing the quality of experience, and the decision information can be further used to instruct the various participants in the streaming media service (user equipment 10, base station 20, and server 30) to perform adjustments to optimize the quality of experience of the streaming media service.

[0031] It should be understood that, in one embodiment of the present disclosure, the AI ​​model may be deployed in any of the user equipment 10, the base station 20, and the server 30. Alternatively, in one embodiment of the present disclosure, the AI ​​model may be deployed in a network device (not shown) that is communicatively connected to the user equipment 10, the base station 20, and the server 30.

[0032] Figure 2 It is a schematic diagram illustrating an AI model for implementing an optimization method for streaming media services according to an embodiment of the present disclosure.

[0033] like Figure 2 As shown, the data collection module 201 provides input data for the model training module 202 and the model prediction module 203, that is, collects training data for model training, and collects data to be predicted for model prediction. For example, the input data can be measurement data obtained from various user devices or different network elements in the network. In addition, the input data can also be the output data predicted by the model, and the feedback result data obtained after the control action.

[0034] The model training module 202 performs training, testing, and verification of the AI ​​model and generates metrics for the performance of the AI ​​model. For example, the model training module 202 can organize the training data provided by the data acquisition module 201 (including data cleaning, data formatting, data conversion, etc.). The model training module 202 can deploy / update the trained, tested, and verified AI model.

[0035] The model prediction module 203 performs output according to the data to be predicted provided by the data acquisition module 201, and the output includes the prediction of the experience quality of a specific service and the decision for optimizing the experience quality. In addition, the model prediction module 203 can also provide model performance feedback to the model training module 202 to verify the performance of the AI ​​model. Similarly, the model prediction module 203 can organize the data to be predicted provided by the data acquisition module 201 (including data cleaning, data formatting, data conversion, etc.).

[0036] The control action module 204 initiates corresponding actions for the participants of a specific service based on the output of the received model prediction module 203. For example, the control action module 204 instructs the participants of a specific service to perform adjustments to optimize the experience quality of the service. In addition, the control action module 204 can provide feedback results to the data acquisition module 201 for training, testing, and verification of the AI ​​model.

[0037] Above, refer to Figure 1 and Figure 2 The scenario and AI model of the optimization method for streaming media service according to the embodiment of the present disclosure are described. Hereinafter, the optimization method for streaming media service according to the embodiment of the present disclosure will be described in detail.

[0038] Figure 3 is a flowchart illustrating an optimization method for streaming media services according to an embodiment of the present disclosure. Figure 3 The optimization method for streaming media services according to the embodiment of the present disclosure can be referred to as Figure 2 The AI ​​model described above is executed, and as mentioned above, the AI ​​model can be deployed in Figure 1 Any of the user equipment 10 , base station 20 and server 30 shown may also be deployed in a network device that is communicatively connected to the user equipment 10 , base station 20 and server 30 .

[0039] In step S301, service information of the streaming media service is obtained.

[0040] In an embodiment of the present disclosure, the service information includes at least one of the following: buffer information for streaming media services, delay information of streaming media services, bit rate information of streaming media services, time information related to changes in experience quality, location information related to changes in experience quality, and wireless channel quality information used by streaming media services.

[0041] More specifically, the buffer information for the streaming service may include the actual buffer size of the user equipment for the streaming service. For example, it may be the average value of the buffer size within a predetermined time period. The buffer information for the streaming service may also include the size of the buffer that was cleared before the buffer was rebuffered, the time point when the buffer rebuffered occurred, and / or the cause of the buffering (for example, but not limited to, bit rate change, channel quality change, video stream error, etc.).

[0042] The latency information of the streaming media service may include the actual initial playback latency of the video stream / streaming media of the user device, that is, the time difference between clicking the video stream / streaming media play button and the actual playback of the video stream / streaming media. For example, it may be each initial playback latency and / or corresponding time recorded by the user device, or the average of multiple initial playback latency.

[0043] The bit rate information of the streaming media service may be a specific value of the video bit rate of the streaming media service used by the user equipment.

[0044] The time information related to the change in the quality of experience may include the duration of each playback interruption or freeze, and may optionally include the time point at which each playback interruption or freeze occurs.

[0045] The location information related to the change in experience quality may include the geographic location of the user device at each playback interruption or freeze.

[0046] The wireless channel quality information used by the streaming media service may include the wireless channel quality measured periodically or at each playback interruption, freeze, or buffer re-buffering moment.

[0047] In an embodiment of the present disclosure, after receiving a request message sent by a network device on which an AI model is deployed (the request message is used to request the user device to report relevant information), the user device may report business information of the streaming media service to the network device; or, the user device actively reports business information of the streaming media service to the network device; or, the user device sends a notification message to the network device (used to notify the network device that the user device has stored business information of the streaming media service), and after receiving the request message sent by the network device, reports business information of the streaming media service stored by the user device to the network device.

[0048] In an embodiment of the present disclosure, the user equipment reports the service information of the streaming media service to the network equipment. The user equipment may obtain, measure, record and store the service information of the streaming media service according to the time period configured by the network equipment. The user equipment may further adopt a one-time reporting, periodic reporting or event-triggered reporting method according to the configuration.

[0049] In step S302, prediction information of the experience quality of the streaming media service is generated based on the service information of the streaming media service.

[0050] In an embodiment of the present disclosure, the prediction information includes at least one of the following: parameter information causing the change in the quality of experience, and time information of the change in the quality of experience.

[0051] More specifically, the parameter information causing the change in the quality of experience may include the quality of experience index that is expected to change (deteriorate / improve) and / or the reason for the change in the quality of experience index (deteriorate / improve). It may be, but is not limited to, one or more of the following: buffer size, i.e., the size of data that can be cached by the user equipment; initial playback delay of the video stream / streaming media, i.e., the time difference between clicking the video stream / streaming media play button and the actual playback of the video stream / streaming media; the quality of experience index visible on the access network base station side, such as the current video bit rate value as the quality of experience index visible on the access network base station side (RV-QoE); bit rate change, i.e., the video stream / streaming media bit rate needs to be adjusted due to channel quality changes and network congestion, causing the buffer to re-buffer, resulting in video stream / streaming media jamming or excessive delay; channel quality change; large cell load or limited transmission resources, i.e., limited allocable transmission resources, resulting in insufficient buffer buffering, resulting in video stream / streaming media jamming or excessive delay; mobility problems such as switching of the user equipment, i.e., service transmission interruption due to switching and other behaviors, resulting in video stream / streaming media jamming or excessive delay.

[0052] The time information of the experience quality change may include the time information of the expected experience quality indicator change (deterioration / improvement) and / or the expected duration of the experience quality indicator change (deterioration / improvement).

[0053] In step S303, optimization information for optimizing the experience quality of the streaming media service is generated based at least on the prediction information.

[0054] In an embodiment of the present disclosure, the optimization information is used to indicate at least one of the following: target bit rate information of the streaming media service, target parameter information of the buffer used for the streaming media service, target delay parameter information of the streaming media service, target time-frequency resource parameter information of the user equipment, indication information of the server sending target bit rate streaming media, and optimization time information of the user equipment, base station and / or server.

[0055] More specifically, the target bitrate information of the streaming media service indicates the maximum bitrate that can ensure smooth playback of the video stream / streaming media under the influence of multiple factors such as channel quality and cell load.

[0056] The target parameter information of the buffer for the streaming media service indicates the minimum buffer size required by the user equipment to ensure continuous playback of the video stream / streaming media at the current bit rate.

[0057] The target delay parameter information of the streaming media service indicates the time difference between clicking the video stream / streaming media play button and the actual playback of the video stream / streaming media, which ensures that the user has a good experience.

[0058] The target time-frequency resource parameter information of the user equipment indicates the minimum value of the time-frequency resource size required by the user equipment under the modulation order reference value or the channel quality reference value; wherein, the modulation order reference value can be a modulation order of a unified value configured by the network equipment or defined by the protocol; the channel quality reference value can be RSRP / RSPQ / SINR, etc., of a unified value configured by the network equipment or defined by the protocol.

[0059] The indication information of the server sending the target bit rate streaming media is the indication information of the server sending the video stream / streaming media data packet corresponding to the target bit rate.

[0060] The optimization time information of the user equipment, base station and / or server indicates the latest time to adjust parameter configuration and resource allocation before the quality experience indicator changes (deteriorates / improves).

[0061] In step S304, optimization information is sent to one or more of the user equipment, a base station for network access of the user equipment, and a server device providing a streaming media service.

[0062] In an embodiment of the present disclosure, optimization information is sent to each participant of the streaming media service (ie, user equipment, base station and server equipment), thereby instructing each participant to perform adjustments to optimize the quality experience of the streaming media service.

[0063] As above Figure 3 The optimization method for streaming media services according to the embodiment of the present disclosure described herein takes into account the impact of the behaviors of various participants in the streaming media services on the quality experience of the streaming media services, rather than just taking the application layer as the optimization object of the streaming media services. In addition, the optimization method for streaming media services according to the embodiment of the present disclosure predicts and determines the optimization strategy of the quality experience in advance before the quality experience of the streaming media services deteriorates, thereby effectively avoiding large fluctuations in the quality experience and ensuring a good user experience.

[0064] Reference above Figure 3 The optimization method for streaming media services according to the embodiment of the present disclosure performed by a network device configured with an AI model is described below. Figure 4 and Figure 5 The optimization method for streaming media services according to the embodiment of the present disclosure and performed by various participants of the streaming media services is described.

[0065] Figure 4 An optimization method for streaming media services according to an embodiment of the present disclosure and executed by a user device is shown.

[0066] In step S401, service information of the streaming media service is reported.

[0067] As described above, the service information includes at least one of the following: buffer information for streaming media services, delay information for streaming media services, bit rate information for streaming media services, time information related to changes in quality of experience, location information related to changes in quality of experience, and quality information of wireless channels used by streaming media services.

[0068] In an embodiment of the present disclosure, after receiving a request message sent by a network device on which an AI model is deployed (the request message is used to request the user device to report relevant information), the user device may report business information of the streaming media service to the network device; or, the user device actively reports business information of the streaming media service to the network device; or, the user device sends a notification message to the network device (used to notify the network device that the user device has stored business information of the streaming media service), and after receiving the request message sent by the network device, reports business information of the streaming media service stored by the user device to the network device.

[0069] In an embodiment of the present disclosure, the user equipment reports the service information of the streaming media service to the network equipment. The user equipment may obtain, measure, record and store the service information of the streaming media service according to the time period configured by the network equipment. The user equipment may further adopt a one-time reporting, periodic reporting or event-triggered reporting method according to the configuration.

[0070] In step S402, optimization information is received.

[0071] As described above, the optimization information is used to indicate at least one of the following: target bit rate information of the streaming service, target parameter information of the buffer used for the streaming service, target delay parameter information of the streaming service, target time-frequency resource parameter information of the user equipment, indication information of the server sending target bit rate streaming media, and optimization time information of the user equipment, base station and / or server.

[0072] In step S403, uplink service transmission is adjusted based on at least the optimization information.

[0073] In an embodiment of the present disclosure, after receiving the optimization information, the user equipment adjusts the uplink service transmission according to the optimization information and other acquired information, for example, adjusting the uplink data transmission packet size according to changes in network channel quality, changes in network load conditions, and expected time points of changes in quality experience indicators.

[0074] As above Figure 4 The optimization method for streaming media services according to the embodiments of the present disclosure achieves optimization of quality experience by having user equipment perform adjustments on quality experience of streaming media services.

[0075] Figure 5An optimization method for streaming media services according to an embodiment of the present disclosure and executed by a base station or a server is shown.

[0076] In step S501, optimization information is received.

[0077] As described above, the optimization information is used to indicate at least one of the following: target bit rate information of the streaming service, target parameter information of the buffer used for the streaming service, target delay parameter information of the streaming service, target time-frequency resource parameter information of the user equipment, indication information of the server sending target bit rate streaming media, and optimization time information of the user equipment, base station and / or server.

[0078] In step S502, parameter configuration and resource allocation adjustment are performed based at least on the optimization information.

[0079] In an embodiment of the present disclosure, after receiving the optimization information, the base station side adjusts the parameter configuration and resource allocation of the quality experience indicator that is expected to deteriorate according to the optimization information and other acquired information.

[0080] If the quality experience indicator that is expected to change is caused by the buffer size and / or the initial playback delay of the video stream / streaming media, the base station side can determine the resource allocation adjustment for the user equipment and the specific value of the adjustment based on the buffer target value and / or the target value of the time-frequency resource size required by the user equipment in the adjustment decision-related information and / or other information.

[0081] If the bit rate of the service is expected to change, the base station side can determine the resource allocation adjustment and the specific value of the adjustment for the user equipment based on the target value of the time-frequency resource size required by the user equipment in the adjustment decision-related information; thereby increasing transmission resources and reducing video stream / streaming media freezes or excessive delays caused by buffer re-buffering.

[0082] If the service is expected to have a large transmission interruption due to the switching of user equipment, the base station side can make resource allocation adjustments and the specific values ​​of the adjustments based on the buffer target value and / or other information in the adjustment decision-related information; thereby increasing the buffer size that can be buffered by the user equipment and avoiding video stream / streaming media freezes or excessive delays caused by transmission interruptions.

[0083] Based on the time information of the expected deterioration / change of the quality experience indicator and / or the expected duration of the deterioration / change of the quality experience indicator and / or the time for starting parameter configuration and resource allocation adjustment in the adjustment decision-related information, or the time for completing the parameter configuration and resource allocation adjustment and / or other information, the base station side determines the time to start the adjustment; thereby, the base station side can complete the corresponding adjustment in time before the quality experience indicator deteriorates / changes, and avoid waste of resources or impact on other user devices caused by premature adjustment.

[0084] If it is predicted that one or more quality experience indicator deterioration / changes of a service / application of a user device cannot be prevented in advance through parameter configuration, resource allocation adjustment, etc., for example, if the number of times that the quality experience indicator deteriorates / changes cannot be prevented in advance through parameter configuration, resource allocation adjustment, etc. reaches a certain proportion in the total number of quality experience indicator deteriorations / changes, the base station side can switch the user device to other base stations or cells to ensure a good quality experience.

[0085] In an embodiment of the present disclosure, after receiving the optimization information, the server makes adjustments according to the optimization information and other acquired information.

[0086] If the server obtains indication information of the video stream / streaming media data packet corresponding to the target bit rate, the server can notify the user device to download and buffer the video stream data packet that meets the target bit rate in advance based on the target bit rate in the adjustment decision-related information; thereby, the user device can buffer the video stream data packet after the bit rate adjustment in advance, avoiding video stream / streaming media freeze or excessive delay caused by buffer re-buffering.

[0087] The server determines the time to start the adjustment based on the time information of the expected quality experience change and / or the expected duration of the quality experience indicator change and / or the time to start the parameter configuration and resource allocation adjustment in the adjustment decision-related information, or the time to complete the parameter configuration and resource allocation adjustment and / or other information. Thus, the server can complete the corresponding adjustment in time before the quality experience indicator deteriorates / changes, and avoid waste of resources caused by premature adjustment.

[0088] Figure 6 is a block diagram illustrating an optimization device for streaming media services according to an embodiment of the present disclosure.

[0089] like Figure 6 As shown, the optimization device 600 for streaming media service according to the embodiment of the present disclosure includes a service information acquisition unit 601, a prediction information generation unit 602, an optimization information generation unit 603 and an optimization information sending unit 604. It is easy for those skilled in the art to understand that these unit modules can be implemented in various ways by hardware alone, software alone or by a combination thereof, and the present disclosure is not limited to any of them. The optimization device 600 for streaming media service according to the embodiment of the present disclosure can be deployed in any of the user equipment 10, the base station 20 and the server 30. Alternatively, in one embodiment of the present disclosure, the optimization device 600 for streaming media service can be deployed in a network device that is communicatively connected to the user equipment 10, the base station 20 and the server 30.

[0090] Specifically, the service information acquisition unit 601 is configured to acquire service information of the streaming media service; the prediction information generation unit 602 is configured to generate prediction information of the experience quality of the streaming media service based on the service information of the streaming media service; the optimization information generation unit 603 is configured to generate optimization information for optimizing the experience quality of the streaming media service based at least on the prediction information; the optimization information sending unit 604 is configured to send optimization information to one or more of a user device, a base station for network access of the user device, and a server device providing the streaming media service.

[0091] The service information includes at least one of the following: buffer information for streaming media services, delay information of streaming media services, bit rate information of streaming media services, time information related to changes in experience quality, location information related to changes in experience quality, and wireless channel quality information used by streaming media services.

[0092] The prediction information includes at least one of the following: parameter information causing the change in the quality of experience, and time information of the change in the quality of experience.

[0093] The optimization information is used to indicate at least one of the following: target bit rate information of the streaming service, target parameter information of the buffer used for the streaming service, target delay parameter information of the streaming service, target time-frequency resource parameter information of the user equipment, indication information of the server sending target bit rate streaming media, and optimization time information of the user equipment, base station and / or server.

[0094] Figure 7 is a hardware block diagram of an electronic device 700 according to an embodiment of the present disclosure. The electronic device according to an embodiment of the present disclosure includes at least a processor; and a memory for storing computer-readable instructions. When the computer-readable instructions are loaded and executed by the processor, the processor executes the optimization method for streaming media services as described above.

[0095] Figure 7 The electronic device 700 shown specifically includes: a central processing unit (CPU) 701, a graphics processing unit (GPU) 702 and a main memory 703. These units are connected to each other via a bus 704. The central processing unit (CPU) 701 and / or the graphics processing unit (GPU) 702 can be used as the above-mentioned processor, and the main memory 703 can be used as the above-mentioned memory for storing computer-readable instructions. In addition, the electronic device 700 may also include a communication unit 705, a storage unit 706, an output unit 707, an input unit 708 and an external device 709, which are also connected to the bus 704.

[0096] Figure 8 is a schematic diagram illustrating a computer-readable storage medium according to an embodiment of the present disclosure. Figure 8As shown, a computer-readable storage medium 800 according to an embodiment of the present disclosure has computer-readable instructions 801 stored thereon. When the computer-readable instructions 801 are executed by a processor, the optimization method for streaming media services described with reference to the above figures is executed. The computer-readable storage medium includes, but is not limited to, for example, volatile memory and / or non-volatile memory. The volatile memory may, for example, include a random access memory (RAM) and / or a cache memory (cache), etc. The non-volatile memory may, for example, include a read-only memory (ROM), a hard disk, a flash memory, an optical disk, a magnetic disk, etc.

[0097] The optimization method, device, electronic device, and storage medium for streaming media services according to the embodiments of the present disclosure are described above with reference to the accompanying drawings. The optimization method for streaming media services according to the embodiments of the present disclosure simultaneously considers the impact of the behaviors of various participants in the streaming media services on the quality experience of the streaming media services, rather than just taking the application layer as the optimization object of the streaming media services. In addition, the optimization method for streaming media services according to the embodiments of the present disclosure predicts and determines the optimization strategy of the quality experience in advance before the quality experience of the streaming media services deteriorates, thereby effectively avoiding large fluctuations in the quality experience and ensuring a good user experience.

[0098] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0099] The basic principles of the present disclosure are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present disclosure are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present disclosure. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, and are not limitations. The above details do not limit the present disclosure to the necessity of adopting the above specific details to be implemented.

[0100] The block diagrams of the devices, apparatuses, equipment, and systems involved in this disclosure are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including," "comprising," "having," and the like are open words, referring to "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or," and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0101] Additionally, as used herein, "or" used in a list of items beginning with "at least one" indicates a separate list, so that, for example, a list of "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not mean that the example described is preferred or better than other examples.

[0102] It should also be noted that in the system and method of the present disclosure, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present disclosure.

[0103] Various changes, substitutions, and modifications of the techniques described herein may be made without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of the present disclosure is not limited to the specific aspects of the processes, machines, manufactures, compositions of events, means, methods, and actions described above. Currently existing or later to be developed processes, machines, manufactures, compositions of events, means, methods, or actions that perform substantially the same functions or achieve substantially the same results as the corresponding aspects described herein may be utilized. Thus, the appended claims include such processes, machines, manufactures, compositions of events, means, methods, or actions within their scope.

[0104] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0105] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A method for optimizing streaming media services, characterized in that: include: Generating prediction information of experience quality of the streaming media service based on the service information of the streaming media service; The prediction information is at least used to indicate the predicted time information of the experience quality change.

2. The optimization method for streaming media services according to claim 1, characterized in that: The method further comprises: Obtain service information of the streaming media service.

3. The optimization method for streaming media service according to claim 1, characterized in that: The method further comprises: At least based on the prediction information, optimization information for optimizing the quality of experience of the streaming media service is generated.

4. The optimization method for streaming media services according to claim 3, characterized in that: The business information includes at least one of the following: Buffer information for the streaming service, delay information of the streaming service, bit rate information of the streaming service, time information related to changes in quality of experience, location information related to changes in quality of experience, and quality information of a wireless channel used by the streaming service.

5. The optimization method for streaming media services according to claim 3, characterized in that: The prediction information includes at least one of the following: Parameter information causing the change in the quality of experience, and time information of the change in the quality of experience.

6. The optimization method for streaming media services according to any one of claims 3 to 5, characterized in that: Also includes: The optimization information is sent to one or more of a user equipment, a base station for network access of the user equipment, and a server device providing the streaming media service.

7. The optimization method for streaming media services according to claim 6, characterized in that: The optimization information is used to indicate at least one of the following: The target bit rate information of the streaming service, the target parameter information of the buffer used for the streaming service, the target delay parameter information of the streaming service, the target time-frequency resource parameter information of the user equipment, the indication information of the server sending the target bit rate streaming media, and the optimization time information of the user equipment, the base station and / or the server.

8. An optimization device for streaming media services, characterized in that: include: A prediction information generating unit, configured to generate prediction information of the experience quality of the streaming media service based on the service information of the streaming media service; The prediction information is at least used to indicate the predicted time information of the experience quality change.

9. The optimization device for streaming media services according to claim 8, characterized in that: The optimization device also includes: a service information acquisition unit, configured to acquire service information of the streaming media service; and The optimization information generating unit is configured to generate optimization information for optimizing the experience quality of the streaming media service based at least on the prediction information.

10. The optimization device for streaming media service according to claim 9, characterized in that: The business information includes at least one of the following: Buffer information for the streaming service, delay information of the streaming service, bit rate information of the streaming service, time information related to changes in quality of experience, location information related to changes in quality of experience, and quality information of a wireless channel used by the streaming service.

11. The optimization device for streaming media service according to claim 9, characterized in that: The prediction information includes at least one of the following: Parameter information causing the change in the quality of experience, and time information of the change in the quality of experience.

12. The optimization device for streaming media services according to any one of claims 9 to 11, characterized in that: Also includes: The optimization information sending unit is configured to send the optimization information to one or more of a user device, a base station for network access of the user device, and a server device providing the streaming media service.

13. The optimization device for streaming media service according to claim 12, characterized in that: The optimization information is used to indicate at least one of the following: The target bit rate information of the streaming service, the target parameter information of the buffer used for the streaming service, the target delay parameter information of the streaming service, the target time-frequency resource parameter information of the user equipment, the indication information of the server sending the target bit rate streaming media, and the optimization time information of the user equipment, the base station and / or the server.

14. An electronic device, characterized in that: include: a memory for storing computer readable instructions; as well as A processor, configured to execute the computer-readable instructions so that the electronic device executes the optimization method for streaming media services as described in any one of claims 1 to 7.

15. A non-transitory computer-readable storage medium for storing computer-readable instructions, characterized in that: When the computer-readable instructions are executed by a processor, the processor executes the optimization method for streaming media services as described in any one of claims 1 to 7.

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