Video adaptive transmission method and device, electronic equipment and storage medium
By predicting the future QoE metrics of wireless video streaming services in the QoE prediction neural network model and optimizing wireless scheduling parameters, the problem of inability to timely and accurately evaluate the quality of user experience in the existing technology is solved, and adaptive video transmission control is realized. Improved user experience.
Patent Information
- Application Number
- CN202310759489.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-06-26
- Publication Date
- 2024-12-27
AI Technical Summary
The prior art cannot evaluate the user experience quality (QoE) of wireless video streaming services in a timely and accurate manner, resulting in the inability to realize adaptive video transmission control, affecting users' subjective feelings about the video service.
By obtaining wireless channel environment parameters and scheduling parameters, input them into the converging QoE prediction neural network model, predict the video transmission QoE metric indicators within the future time, and optimize the wireless scheduling parameters based on the prediction results to achieve the preset QoE standard.
It realizes timely and accurate evaluation and adaptive control of wireless video streaming services, improves users' subjective feelings about video services, and reduces the consumption of upstream resources.
Smart Images
Figure CN119211613A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of communication technology, and in particular to a method, device, electronic device and storage medium for adaptively transmitting a video. Background Art
[0002] With the development of communication technology and mobile terminal devices, new video services (such as augmented reality AR, virtual reality VR, etc.) are widely used in various industries such as education, film and television, medical care and games, bringing people a rich experience. For the above video services, if a good user experience cannot be provided, a large number of customers will be lost.
[0003] Therefore, optimizing user experience has become one of the main goals of network operators (including equipment suppliers, video service providers, and telecom operators). The ultimate goal of wireless video streaming services is to provide users with satisfactory quality of experience. The International Telecommunication Union Standardization Organization defines Quality of Experience (QoE) as an indicator to measure users' subjective feelings. It refers to the overall acceptability of an application or service perceived by end users, which can directly reflect users' subjective feelings about using the service. QoE helps network operators understand the influencing parameters that are closely related to user satisfaction, and ultimately improves user loyalty to maintain advantages in the fierce market competition. Therefore, the configuration of wireless scheduling parameters for wireless video streaming services based on QoE is not only the focus of academic research, but also the key for network operators to ensure good quality of experience in order to retain and expand the scale of users.
[0004] However, the current method for optimizing user experience QoE is mainly to optimize user experience by actively feedback from users on video playback status. This method consumes a lot of uplink resources and cannot provide real-time feedback, making it impossible to timely and accurately evaluate the QoE of wireless video streaming services and perform adaptive video transmission control to improve users' subjective experience of video services. Summary of the invention
[0005] The main purpose of the present application is to provide a method, device, electronic device and storage medium for adaptive transmission of video, aiming to timely and accurately evaluate the QoE of wireless video streaming services and perform adaptive video transmission control.
[0006] To achieve the above object, the present application provides a video adaptive transmission method, comprising: Obtaining wireless channel environment parameters and wireless scheduling parameters of the current period; Inputting the wireless channel environment parameters and wireless scheduling parameters of the preset target time series into the converged QoE prediction neural network model, and predicting the QoE metric index of video transmission within a future preset time period, wherein the preset target time series includes the current period and the preset time period before the current period; According to the predicted QoE metric, the wireless scheduling parameters of the next cycle are optimized and adjusted so that the QoE metric of the next cycle reaches the preset QoE standard for transmission.
[0007] In addition, to achieve the above-mentioned purpose, the present application also provides a video adaptive transmission device, including: A data acquisition module, configured to obtain wireless channel environment parameters and wireless scheduling parameters of the current period; A QoE prediction module is configured to input wireless channel environment parameters and wireless scheduling parameters of a preset target time series into a converged QoE prediction neural network model to predict a QoE metric for video transmission within a future preset duration, wherein the preset target time series includes a current period and a preset duration before the current period; The wireless scheduling optimization module is configured to optimize and adjust the wireless scheduling parameters of the next cycle according to the predicted QoE metric index, so that the QoE metric index of the next cycle reaches the preset QoE standard for transmission.
[0008] In addition, to achieve the above-mentioned purpose, the present application also provides an electronic device, which includes: a memory, a processor, and an adaptive transmission program for video stored in the memory and run on the processor, and when the adaptive transmission program for video is executed by the processor, the adaptive transmission method for video as described above is implemented.
[0009] In addition, to achieve the above-mentioned purpose, the present application also provides a storage medium, which is a computer-readable storage medium, and a video adaptive transmission program is stored on the computer-readable storage medium. When the video adaptive transmission program is executed by a processor, the video adaptive transmission method as described above is implemented.
[0010] The present application proposes a method, device, electronic device and storage medium for adaptive transmission of a video. In the adaptive transmission method of a video, the technical solution of the embodiment of the present application is to obtain wireless channel environment parameters and wireless scheduling parameters of the current cycle, and input the wireless channel environment parameters and wireless scheduling parameters of a preset target time series (including the current cycle and the preset duration before the current cycle) into a converged QoE prediction neural network model, predict the QoE metric index of video transmission within the future preset duration (which may include at least one of the number of freezes, freeze time, the number of resolution switching and the resolution after switching), and then optimize and adjust the wireless scheduling parameters of the next cycle according to the predicted QoE metric index, so that the QoE metric index of the next cycle reaches the preset QoE standard for transmission, thereby realizing the QoE metric index predicted by the QoE prediction neural network model, executing the QoE wireless scheduling strategy, adjusting the wireless scheduling parameters, and improving the user QoE of the video transmission service.
[0011] Compared with the current method of optimizing user QoE by actively providing feedback on video playback status by the user, the embodiment of the present application does not need to consume a large amount of uplink resources and can provide real-time feedback, so that the embodiment of the present application can timely and accurately evaluate the QoE of the wireless video streaming service, and optimize and adjust the current wireless scheduling parameters according to the evaluated QoE (i.e., the predicted QoE metric), thereby realizing adaptive video transmission control and improving the user's subjective experience of the video service. BRIEF DESCRIPTION OF THE DRAWINGS
[0012] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.
[0013] Figure 1 This is a flow chart of the first embodiment of the adaptive video transmission method of the present application; Figure 2 A schematic diagram of a training framework of a QoE prediction neural network model in an example of the present application; Figure 3 A schematic diagram of the architecture of an adaptive video transmission method in an example of the present application; Figure 4 A schematic diagram of a process framework of an adaptive video transmission method in an example of the present application; Figure 5 A schematic diagram of the functional modules of the adaptive transmission device of the video according to the embodiment of the present application; Figure 6 This is a schematic diagram of the hardware structure of the electronic device involved in the embodiment of the present application.
[0014] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION
[0015] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.
[0016] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.
[0017] It should be noted that all directional indications in the embodiments of the present application (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.
[0018] In this application, unless otherwise clearly specified and limited, the terms "connection", "fixation", etc. should be understood in a broad sense. For example, "fixation" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.
[0019] In addition, in this application, descriptions such as "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in this field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.
[0020] At present, the method for optimizing user experience QoE (Quality of Experience) is mainly to optimize user experience by actively feedback the video playback status by users. This method consumes a lot of uplink resources and cannot provide real-time feedback. Therefore, it is impossible to timely and accurately evaluate the QoE of wireless video streaming services and perform adaptive video transmission control to improve users' subjective experience of video services.
[0021] Based on this, the embodiment of the present application provides a method for adaptively transmitting a video. Figure 1 , Figure 1 The following is a flow chart of an embodiment of a method for adaptively transmitting a video according to the present application. In this embodiment, the method for adaptively transmitting a video includes: Step S10, obtaining wireless channel environment parameters and wireless scheduling parameters of the current period; In this embodiment, the wireless channel environment parameter refers to a channel environment characteristic parameter of a wireless channel, and the wireless channel environment parameter may include channel quality indication CQI (Channel Quality Indication) information, signal to interference plus noise ratio SINR (Signal to Interference plus Noise Ratio, signal to interference plus noise ratio) information, reference signal received power RSRP (Reference Signal Receiving Power, reference signal received power) information, path loss PL (Path Loss, path loss) information, and base station transmit power information.
[0022] Exemplarily, the wireless scheduling parameter refers to a policy parameter for scheduling and optimizing wireless channel environment parameters, and the wireless scheduling parameter may include modulation and coding strategy MCS (Modulation and Coding Scheme, modulation and coding strategy) information, and resource block RB (Resource Block, resource block) quantity information.
[0023] Step S20, inputting the wireless channel environment parameters and wireless scheduling parameters of the preset target time series into the converged QoE prediction neural network model, and predicting the QoE metric index of video transmission within a future preset time period; The preset target time sequence includes the current cycle and the preset duration before the current cycle. That is to say, the wireless channel environment parameters and wireless scheduling parameters of the preset target time sequence refer to: the wireless channel environment parameters and wireless scheduling parameters of the current cycle, and the wireless channel environment parameters and wireless scheduling parameters of the preset duration before the current cycle. It should be noted that the preset duration can be equal to the duration of one cycle or the duration of multiple cycles, and this embodiment does not make specific limitations on this.
[0024] Those skilled in the art can understand that the current cycle refers to the cycle of obtaining the current wireless channel environment parameters and wireless scheduling parameters for optimization scheduling, that is, "obtaining wireless channel environment parameters and wireless scheduling parameters" should be periodically obtained, and "inputting wireless channel environment parameters and wireless scheduling parameters into the converged QoE prediction neural network model to predict the QoE metric indicators for video transmission within the future preset duration" should also be periodically predicted. Before the next cycle adjacent to each cycle arrives, it is necessary to predict the QoE metric indicators for video transmission within the future preset duration, so as to perform periodic iterative optimization of the wireless scheduling parameters based on the QoE metric indicators predicted in each cycle.
[0025] In this embodiment, the types of QoE metrics may include at least one of the number of freezes, freeze duration, number of switches, and switching resolution. It is known to those skilled in the art that the types of QoE metrics can be defined by analyzing the R17 protocol. It is easy to understand that for different video services, it is necessary to consider which QoE metrics to collect and how to deploy monitoring and real-time collection of these data in the system. Currently, existing terminals do not support accurate real-time collection of video playback status information (including QoE metrics), but the R17 protocol defines the collection of real-time playback status information of terminals.
[0026] In view of the lack of objective measurement and prediction of QoE of video services in the existing network, the technical means of QoE measurement and prediction of video services are still imperfect, and the problem of optimizing QoE based on scheduling means from the network side is still unclear, the embodiment of the present application collects wireless channel environment parameters and wireless scheduling parameters from the base station side to optimize the scheduling strategy and adjust the wireless scheduling parameters, which can effectively ensure the QoE of users in video services. Figure 4 . In this embodiment, the QoE prediction neural network model is deployed on the base station side. The base station only needs to collect wireless channel environment parameters and wireless scheduling parameters to predict the user's quality of experience in real time without the user terminal's real-time feedback of the video playback status (reducing uplink resource consumption). It can make better video transmission scheduling decisions in advance and improve the user's quality of experience.
[0027] Exemplarily, the type of the QoE prediction neural network model may be an LSTM (Long Short-Term Memory) neural network model, a Transform neural network model, a Kmeans clustering neural network model, or a time series neural network model.
[0028] In this embodiment, the preset duration may be equal to the duration of one cycle, and of course, may also be equal to the duration of multiple cycles. Those skilled in the art may set it according to actual conditions. This embodiment does not make any specific limitation on this, so as to better (accurately and sensitively) predict the QoE metric indicators for future video transmission.
[0029] Furthermore, the types of QoE metrics may include the number of freezes, freeze duration, number of switches and switching resolution. By setting different weights for the number of freezes, freeze duration, number of switches and switching resolution, the various QoE metrics are weightedly combined and input into the QoE prediction neural network model.
[0030] After step S20, step S30 is executed to optimize and adjust the wireless scheduling parameters of the next cycle according to the predicted QoE metric index, so that the QoE metric index of the next cycle reaches the preset QoE standard for transmission.
[0031] In this embodiment, after obtaining the wireless channel environment parameters and wireless scheduling parameters of the current period, the wireless channel environment parameters and wireless scheduling parameters of the preset target time series are input into the QoE prediction neural network model to predict the QoE metric index, so as to optimize and adjust the wireless scheduling parameters of the next period according to the predicted QoE metric index, so that the QoE metric index of the next period reaches the preset QoE standard for transmission, thereby realizing adaptive adjustment of video stream transmission control, thereby effectively combating network fluctuations while effectively improving the video playback effect. It is easy to understand that in wireless transmission, the main factor affecting QoE is the wireless network environment. In order to provide users with better experience quality, the QoE prediction neural network model can be used to efficiently and accurately establish the mapping relationship between the wireless channel environment parameters, the wireless scheduling parameters and the QoE metric index, so as to realize that after predicting the QoE metric index, the optimization strategy is executed according to the predicted QoE metric index to adjust the wireless scheduling parameters to improve the user QoE.
[0032] It is easy to understand that the preset QoE standard for better user experience quality during video playback can be defined based on the DASH (Dynamic Adaptive Streaming over HTTP) video transmission protocol and the user's intuitive feeling of watching videos.
[0033] It should be noted that the QoE prediction neural network model is pre-trained through a learning and training process, that is, a converged QoE prediction neural network model, which can accurately predict the QoE metric indicators for the next cycle based on the wireless channel environment parameters and wireless scheduling parameters of the preset target time series.
[0034] The application environment of the embodiment of the present application may be a video transmission adaptive optimization scenario based on QoE prediction for NR (New Radio), LTE (Long Term Evolution), FDD (Frequency-division Duplex) or TDD (Time-division Duplex). This embodiment can collect the wireless channel status (wireless channel environment parameters) and scheduling information (i.e., wireless scheduling parameters) on the base station side, use the QoE prediction neural network model to predict the user QoE in real time, execute the optimization strategy according to the QoE prediction value, adjust the wireless scheduling parameters, and thus improve the user QoE.
[0035] Please refer to Figure 1 and Figure 3 The technical solution of the embodiment of the present application is to obtain the wireless channel environment parameters and wireless scheduling parameters of the current period, and input the wireless channel environment parameters and wireless scheduling parameters of the preset target time series (including the current period and the preset time before the current period) into the converged QoE prediction neural network model, predict the QoE metric indicators of video transmission within the future preset time period (which may include at least one of the number of freezes, freeze time, the number of resolution switching and the resolution after switching), and then optimize and adjust the wireless scheduling parameters of the next period according to the predicted QoE metric indicators, so that the QoE metric indicators of the next period reach the preset QoE standard for transmission, thereby realizing the QoE metric indicators predicted by the QoE prediction neural network model, executing the QoE wireless scheduling strategy, adjusting the wireless scheduling parameters, and improving the user QoE of the video transmission service.
[0036] Compared with the current method of optimizing user QoE by actively providing feedback on video playback status by the user, the embodiment of the present application does not need to consume a large amount of uplink resources and can provide real-time feedback, so that the embodiment of the present application can timely and accurately evaluate the QoE of the wireless video streaming service, and optimize and adjust the current wireless scheduling parameters according to the evaluated QoE (i.e., the predicted QoE metric), thereby realizing adaptive video transmission control and improving the user's subjective experience of the video service.
[0037] In a possible implementation manner, before the step of optimizing and adjusting the wireless scheduling parameters of the next period, the method further includes: Step A10, determining whether the predicted QoE metric meets the preset QoE standard; Exemplarily, the QoE metric includes the number of freezes, the freeze duration, the number of resolution switches, and the resolution value after switching. The step A10 of determining whether the predicted QoE metric meets the preset QoE standard includes: Step B10, when it is determined that the number of freezes reaches a first preset number threshold, the freeze duration reaches a preset duration threshold, the number of switches reaches a second preset number threshold, or the resolution value after switching is less than a preset resolution threshold, determining that the predicted QoE metric indicator does not meet the preset QoE standard; Step B20, when it is determined that the number of freezes does not reach the first preset number threshold, the freeze duration does not reach the preset duration threshold, the number of switches does not reach the second preset number threshold, and the resolution value after switching is not less than the preset resolution threshold, it is determined that the predicted QoE measurement indicator meets the preset QoE standard.
[0038] In this embodiment, the first preset number threshold, the preset duration threshold, the second preset number threshold and the preset resolution threshold can be set by those skilled in the art according to actual conditions. This embodiment does not make specific limitations, so as to more accurately detect whether the predicted QoE metric meets the preset QoE standard.
[0039] After step A10, step A20 is performed. When it is determined that the predicted QoE metric index does not meet the preset QoE standard, the step of optimizing and adjusting the wireless scheduling parameters of the next period is performed.
[0040] In this embodiment, the preset QoE standard is not specifically limited in this embodiment, and is based on providing the best user QoE as much as possible on the basis of reducing the network channel environment requirements.
[0041] This embodiment performs subsequent steps such as optimizing and adjusting the wireless scheduling parameters for the next cycle only when it is determined that the predicted QoE metric index does not meet the preset QoE standard, thereby improving the user QoE on the basis of minimizing the operating load of wireless scheduling optimization, and maintaining a smooth video playback effect when the network signal is unstable.
[0042] Based on the first embodiment of the present application, a second embodiment of the method for adaptively transmitting a video of the present application is proposed. In this embodiment, the method further includes: Step C10, using the wireless channel environment parameters and wireless scheduling parameters sampled in a preset time series as learning samples, and using the QoE metric associated with video transmission based on the preset time series as a sample label corresponding to the learning sample; Step C20, associating the learning sample with the sample label to obtain a sample set; Step C30: Perform supervised learning on the loss function of the QoE prediction neural network model using the sample set to obtain a converged QoE prediction neural network model.
[0043] Exemplarily, the step of performing supervised learning on the loss function of the QoE prediction neural network model through the sample set to obtain a converged QoE prediction neural network model includes: Step D10, dividing the sample set into a training sample set and a verification sample set; Step D20, training the loss function of the QoE prediction neural network model through the training sample set, and verifying the prediction accuracy of the QoE prediction neural network model through the verification sample set, until the prediction accuracy reaches a preset ratio threshold, stopping the training, and obtaining a converged QoE prediction neural network model.
[0044] In this embodiment, the wireless channel environment parameters and wireless scheduling parameters sampled in a preset time series are used as learning samples, and the QoE measurement index associated with video transmission based on the preset time series is used as a sample label corresponding to the learning sample, and then the learning sample and the sample label are associated to obtain a sample set, and then the loss function of the QoE prediction neural network model is supervised and learned through the sample set, so as to accurately obtain a converged QoE prediction neural network model, so as to facilitate the subsequent efficient and accurate establishment of a mapping relationship between the wireless channel environment parameters, the wireless scheduling parameters and the QoE measurement index according to the converged QoE prediction neural network model, and then realize the prediction of the QoE measurement index of the future preset time length, and execute the optimization strategy according to the predicted QoE measurement index to adjust the wireless scheduling parameters to improve the QoE. The optimization process is not completed offline once, but is repeatedly optimized online.
[0045] In order to help understand the technical concept or technical principle of the embodiment of the present application, a specific embodiment of the video adaptive transmission method is listed, including the following main process steps: Step 1: Data Collection During the video service playback process on the client, wireless channel environment parameters and wireless scheduling parameters are collected respectively. Wireless channel environment parameters and wireless scheduling parameters can be directly obtained on the base station side, as described below: Wireless channel environment parameters mainly describe the state of the wireless channel environment during video transmission. These parameters include but are not limited to CQI information, RSRP information, PL information, SINR information, and base station transmission power information. Wireless scheduling parameters mainly describe the scheduling decisions made by the base station based on the wireless channel environment state for better data transmission. Wireless scheduling parameters include but are not limited to MCS information and RB allocation quantity information.
[0046] The QoE metric mainly describes the user's intuitive feeling when watching videos. The user QoE metric of different video services is different. However, most of the current video services are based on the DASH video transmission protocol, which can adaptively switch the video definition version according to the network environment and transmission resources. Therefore, this embodiment defines the general QoE metric of video services based on the DASH protocol based on the user's intuitive feeling (stuttering) when watching videos and the DASH transmission protocol (switching). These metrics include the number of stutters, the stuttering time, the number of switches, and the switching resolution, which are defined as follows: The number of stutters refers to the number of stutters counted by the client during video playback. The stuttering time refers to the stuttering time counted by the client during video playback. The number of switches refers to the number of video resolution switches counted by the client during video playback. The switching resolution refers to the resolution before and after the switching counted by the client during video playback. These metrics could not be obtained in real time and accurately in the past. After the R17 (TS26.247) protocol was released, the protocol defined a terminal quality report feedback mechanism, and these metrics can be obtained through the Play List in the quality report.
[0047] Step 2: QoE prediction The QoE prediction module consists of two parts: time series neural network model training and QoE prediction.
[0048] The training process of the temporal neural network model is as follows: Step 2.1 Based on the data collected in step 1, construct a time series from two dimensions: wireless channel environment parameters and wireless scheduling parameters as model input, use the QoE metric corresponding to the time series as a label, and construct a data set (training set and test set).
[0049] Step 2.2 Time series neural network training: Use the training set constructed in step 2.1 to train the time series neural network model, and use the test set to test the model. The training framework is as follows: Figure 2 shown.
[0050] Step 2.3 deploys the trained model on the base station side. The base station collects time series data (wireless channel environment parameters and wireless scheduling parameters) over the past period of time in real time as the input of the time series neural network model. The model output is the user's QoE measurement indicator in the future period of time.
[0051] Step 3: QoE wireless scheduling optimization Based on the QoE metrics predicted in step 2 (number of freezes, freeze duration, number of switches, and switching resolution), an optimization strategy is implemented to adjust wireless scheduling parameters to improve the QoE of the video transmission service.
[0052] Please refer to Figure 3 In this embodiment, a mapping relationship between wireless channel environment parameters, wireless scheduling parameters and QoE measurement indicators is established by training a neural network, and the collected wireless channel environment parameters and wireless scheduling parameters are used as inputs of the neural network, and the QoE measurement indicators are used as labels for time series neural network training.
[0053] Please refer to Figure 4 In this embodiment, the trained neural network model is rooted in the resource scheduling system of the base station. By inputting the time series data (wireless channel environment parameters and wireless scheduling parameters) in the past period of time into the neural network model, the user QoE metric index in the future period of time is predicted, and then the wireless resource scheduling parameters are optimized according to the predicted QoE metric index, thereby improving the user's QoE.
[0054] It should be noted that what is disclosed above is only a preferred embodiment of the present application, and it certainly cannot be used to limit the protection scope of the present application. Ordinary technicians in this field can understand that all or part of the processes of implementing the above embodiment and the equivalent changes made according to the claims of the present invention are still within the scope covered by the present application.
[0055] That is to say, the above specific embodiments are only used to help understand the technical concept of the embodiments of the present application, and do not constitute a limitation of the present application. More simple transformations based on the technical concept should all be within the scope of protection of the present application.
[0056] In addition, the embodiment of the present application also proposes a video adaptive transmission device, referring to Figure 5 , Figure 5 This is a functional module diagram of an embodiment of a video adaptive transmission device of the present application.
[0057] In this embodiment, the video adaptive transmission device includes: The data acquisition module 10 is configured to obtain the wireless channel environment parameters and wireless scheduling parameters of the current period; The QoE prediction module 20 is configured to input the wireless channel environment parameters and wireless scheduling parameters of the preset target time series into the converged QoE prediction neural network model to predict the QoE metric index of video transmission within a future preset time period, wherein the preset target time series includes a current period and a preset time period before the current period; The wireless scheduling optimization module 30 is configured to optimize and adjust the wireless scheduling parameters of the next cycle according to the predicted QoE metric index, so that the QoE metric index of the next cycle reaches the preset QoE standard for transmission.
[0058] In some embodiments, the wireless scheduling optimization module 30 is further configured to: Determine whether the predicted QoE metric meets the preset QoE standard; When it is determined that the predicted QoE metric index does not reach the preset QoE standard, the step of optimizing and adjusting the wireless scheduling parameters of the next period is performed.
[0059] In some embodiments, the QoE metric includes the number of freezes, the freeze duration, the number of resolution switches, and the resolution value after switching. The QoE prediction module 20 is further configured as: When it is determined that the number of freezes reaches a first preset number threshold, the freeze duration reaches a preset duration threshold, the number of switches reaches a second preset number threshold, or the resolution value after switching is less than a preset resolution threshold, it is determined that the predicted QoE metric indicator does not meet the preset QoE standard; When it is determined that the number of freezes does not reach the first preset number threshold, the freeze duration does not reach the preset duration threshold, the number of switches does not reach the second preset number threshold, and the resolution value after switching is not less than the preset resolution threshold, it is determined that the predicted QoE measurement indicator meets the preset QoE standard.
[0060] In some embodiments, the video adaptive transmission device further includes a training module (not shown), configured to: Using the wireless channel environment parameters and wireless scheduling parameters sampled in a preset time series as learning samples, and using the QoE metric associated with video transmission based on the preset time series as sample labels corresponding to the learning samples; Associating the learning sample with the sample label to obtain a sample set; Through the sample set, supervised learning is performed on the loss function of the QoE prediction neural network model to obtain a converged QoE prediction neural network model.
[0061] In some embodiments, the training module is further configured to: Dividing the sample set into a training sample set and a validation sample set; The loss function of the QoE prediction neural network model is trained through the training sample set, and the prediction accuracy of the QoE prediction neural network model is verified through the verification sample set, until the prediction accuracy reaches a preset ratio threshold and the training is stopped to obtain a converged QoE prediction neural network model.
[0062] In some embodiments, the wireless scheduling parameters include modulation and coding strategy MCS information and resource block RB quantity information.
[0063] In some embodiments, the wireless channel environment parameters include channel quality indication CQI information, signal to interference plus noise ratio SINR information, reference signal received power RSRP information, path loss PL information, and base station transmit power information.
[0064] The adaptive transmission device for video provided in this embodiment and the adaptive transmission method for video provided in the above-mentioned embodiment belong to the same inventive concept. For technical details not fully described in this embodiment, please refer to the embodiment of the adaptive transmission method for video described above. This embodiment has the same beneficial effects as the embodiments of the adaptive transmission method for video, which will not be repeated here.
[0065] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0066] In addition, an embodiment of the present application also provides an electronic device, which may be, for example, an edge router, or a broadband remote access server (Broadband Remote Access Server, BRAS), a broadband network gateway (Broadband Network Gateway), a serving GPRS support node (Serving GPRS Support Node, SGSN), a gateway GPRS support node (Gateway GPRS Support Node, GGSN), a mobility management entity (Mobility Management Entity, MME) or a serving gateway (Serving GateWay, S-GW), etc.
[0067] Reference Figure 6 , Figure 6 The hardware structure diagram of an electronic device provided in an embodiment of the present application is shown in FIG. Figure 6As shown, the electronic device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (WIreless-FIdelity, WI-FI) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM) memory, or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.
[0068] Those skilled in the art will understand that Figure 6 The structure shown in the figure does not constitute a limitation on the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. Figure 6 As shown, the memory 1005 as a storage medium may include an operating system, a data storage module, a network communication module, a user interface module, and a video adaptive transmission program.
[0069] exist Figure 6 In the electronic device shown, the network interface 1004 is mainly used for data communication with other devices; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in this embodiment can be set in the communication device, and the communication device calls the adaptive transmission program of the video stored in the memory 1005 through the processor 1001, and executes the adaptive transmission method applied to the video provided in any of the above embodiments.
[0070] The terminal proposed in this embodiment and the adaptive transmission method applied to video proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in this embodiment can be referred to any of the above embodiments, and this embodiment has the same beneficial effects as executing the adaptive transmission method for video.
[0071] In addition, an embodiment of the present application also proposes a storage medium, which is a computer storage medium. The computer-readable storage medium may be a non-volatile computer-readable storage medium. The computer-readable storage medium stores an adaptive transmission program for a video. When the adaptive transmission program for a video is executed by a processor, the adaptive transmission method for a video of the present application as described above is implemented.
[0072] The various embodiments of the electronic device and computer-readable storage medium of the present application can all refer to the various embodiments of the adaptive transmission method of the video of the present application, which will not be repeated here.
[0073] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.
[0074] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.
[0075] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for an electronic device to execute the methods described in each embodiment of the present application.
[0076] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.
Claims
1. A video adaptive transmission method, characterized in that: include: Obtaining wireless channel environment parameters and wireless scheduling parameters of the current period; Inputting the wireless channel environment parameters and wireless scheduling parameters of the preset target time series into the converged QoE prediction neural network model, and predicting the QoE metric index of video transmission within a future preset time period, wherein the preset target time series includes the current period and the preset time period before the current period; According to the predicted QoE metric, the wireless scheduling parameters of the next cycle are optimized and adjusted so that the QoE metric of the next cycle reaches the preset QoE standard for transmission.
2. The method for adaptively transmitting a video according to claim 1, wherein: Before the step of optimizing and adjusting the wireless scheduling parameters of the next period, the method further includes: Determine whether the predicted QoE metric meets the preset QoE standard; When it is determined that the predicted QoE metric index does not reach the preset QoE standard, the step of optimizing and adjusting the wireless scheduling parameters of the next period is performed.
3. The method for adaptively transmitting a video according to claim 2, wherein: The QoE metric includes the number of freezes, the freeze duration, the number of resolution switchings, and the resolution value after switching. The step of determining whether the predicted QoE metric reaches the preset QoE standard includes: When it is determined that the number of freezes reaches a first preset number threshold, the freeze duration reaches a preset duration threshold, the number of switches reaches a second preset number threshold, or the resolution value after switching is less than a preset resolution threshold, it is determined that the predicted QoE metric indicator does not meet the preset QoE standard; When it is determined that the number of freezes does not reach the first preset number threshold, the freeze duration does not reach the preset duration threshold, the number of switches does not reach the second preset number threshold, and the resolution value after switching is not less than the preset resolution threshold, it is determined that the predicted QoE measurement indicator meets the preset QoE standard.
4. The method for adaptively transmitting a video according to claim 1, wherein: The method further comprises: Using the wireless channel environment parameters and wireless scheduling parameters sampled in a preset time series as learning samples, and using the QoE metric associated with video transmission based on the preset time series as sample labels corresponding to the learning samples; Associating the learning sample with the sample label to obtain a sample set; Through the sample set, supervised learning is performed on the loss function of the QoE prediction neural network model to obtain a converged QoE prediction neural network model.
5. The method for adaptively transmitting a video according to claim 4, wherein: The step of performing supervised learning on the loss function of the QoE prediction neural network model through the sample set to obtain a converged QoE prediction neural network model comprises: Dividing the sample set into a training sample set and a validation sample set; The loss function of the QoE prediction neural network model is trained through the training sample set, and the prediction accuracy of the QoE prediction neural network model is verified through the verification sample set, until the prediction accuracy reaches a preset ratio threshold and the training is stopped to obtain a converged QoE prediction neural network model.
6. The method for adaptively transmitting a video according to any one of claims 1 to 5, characterized in that: The wireless scheduling parameters include modulation and coding strategy MCS information and resource block RB quantity information.
7. The method for adaptively transmitting a video according to any one of claims 1 to 5, characterized in that: The wireless channel environment parameters include channel quality indication CQI information, signal to interference plus noise ratio SINR information, reference signal received power RSRP information, path loss PL information, and base station transmission power information.
8. A video adaptive transmission device, characterized in that: include: A data acquisition module, configured to obtain wireless channel environment parameters and wireless scheduling parameters of the current period; A QoE prediction module is configured to input wireless channel environment parameters and wireless scheduling parameters of a preset target time series into a converged QoE prediction neural network model to predict a QoE metric for video transmission within a future preset duration, wherein the preset target time series includes a current period and a preset duration before the current period; The wireless scheduling optimization module is configured to optimize and adjust the wireless scheduling parameters of the next cycle according to the predicted QoE metric index, so that the QoE metric index of the next cycle reaches the preset QoE standard for transmission.
9. An electronic device, characterized in that: include: A memory, a processor, and a video adaptive transmission program stored in the memory and executable on the processor, wherein the video adaptive transmission program, when executed by the processor, implements the video adaptive transmission method according to any one of claims 1 to 7.
10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, on which a video adaptive transmission program is stored. When the video adaptive transmission program is executed by a processor, the video adaptive transmission method according to any one of claims 1 to 7 is implemented.