Adaptive bandwidth adjustment audio and video communication quality optimization method and system

Abstract

The invention discloses an adaptive bandwidth adjustment audio and video communication quality optimization method and system, and relates to the technical field of communication. The method comprises the following steps: acquiring an audio and video communication task of a network; monitoring bandwidth parameters of the network in real time to obtain a network bandwidth monitoring result; performing transmission parameter decision on the to-be-transmitted audio and video to generate a first communication transmission scheme; carrying out bandwidth characteristic prediction on the network to obtain a network bandwidth prediction curve; performing multi-node communication quality influence prediction on the to-be-transmitted audio and video to obtain a multi-node communication quality influence prediction result; performing adaptive adjustment on the first communication transmission scheme to obtain a second communication transmission scheme; and based on the network, executing the audio and video communication task according to the communication transmission second scheme. The technical problem that in the prior art, the transmission quality of audio and video communication is unstable when the network bandwidth fluctuates is solved, audio and video communication quality optimization is achieved through self-adaptive bandwidth adjustment, and the technical effect of improving the smoothness and stability of the communication process is achieved.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a method and system for optimizing audio and video communication quality by adaptive bandwidth adjustment. Background Art

[0002] With the rapid development and popularization of the Internet, audio and video communication has become an indispensable part of people's daily life and work. Whether it is remote conferencing, online education, or entertainment live broadcast, audio and video communication plays a vital role. However, in practical applications, the quality of audio and video communication is often affected by many factors, among which the fluctuation of network bandwidth is a particularly critical issue. Existing audio and video communication systems are often affected by network bandwidth fluctuations during network transmission, resulting in problems such as reduced audio and video quality, screen freezes, and increased latency. This problem is particularly prominent in complex network environments, such as multi-node communication scenarios or when bandwidth resources are limited. Current solutions usually rely on fixed transmission parameter settings or simple bandwidth monitoring adjustments, but lack the ability to predict network bandwidth change trends and make it difficult to respond to network fluctuations in a timely manner. Summary of the invention

[0003] The present application provides an audio and video communication quality optimization method and system with adaptive bandwidth adjustment, which solves the technical problem in the prior art that the transmission quality of audio and video communication is unstable when the network bandwidth fluctuates.

[0004] In view of the above problems, the present application provides an audio and video communication quality optimization method and system with adaptive bandwidth adjustment.

[0005] In a first aspect of the present application, a method for optimizing audio and video communication quality with adaptive bandwidth adjustment is provided, the method comprising:

[0006] Obtain an audio and video communication task of a network, wherein the audio and video communication task includes audio and video to be transmitted; monitor the bandwidth parameters of the network in real time to obtain a network bandwidth monitoring result; make a transmission parameter decision for the audio and video to be transmitted based on the network bandwidth monitoring result to generate a first communication transmission plan; predict the bandwidth characteristics of the network according to the network bandwidth monitoring result to obtain a network bandwidth prediction curve; predict the multi-node communication quality impact of the audio and video to be transmitted according to the network bandwidth prediction curve to obtain a multi-node communication quality impact prediction result; adaptively adjust the first communication transmission plan according to the multi-node communication quality impact prediction result to obtain a second communication transmission plan; and execute the audio and video communication task according to the second communication transmission plan based on the network.

[0007] A second aspect of the present application provides an audio and video communication quality optimization system with adaptive bandwidth adjustment, the system comprising:

[0008] Task acquisition module: obtains the audio and video communication task of the network, wherein the audio and video communication task includes the audio and video to be transmitted; network bandwidth monitoring module: monitors the bandwidth parameters of the network in real time to obtain the network bandwidth monitoring result; transmission parameter decision module: makes transmission parameter decision for the audio and video to be transmitted based on the network bandwidth monitoring result, and generates a first communication transmission scheme; network bandwidth prediction module: predicts the bandwidth characteristics of the network according to the network bandwidth monitoring result, and obtains a network bandwidth prediction curve; communication quality prediction module: predicts the multi-node communication quality impact of the audio and video to be transmitted according to the network bandwidth prediction curve, and obtains a multi-node communication quality impact prediction result; adaptive adjustment module: adaptively adjusts the first communication transmission scheme according to the multi-node communication quality impact prediction result, and obtains a second communication transmission scheme; communication task execution module: executes the audio and video communication task according to the second communication transmission scheme based on the network.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] First, the audio and video communication task of the network is obtained, wherein the audio and video communication task includes the audio and video to be transmitted. Next, the bandwidth parameters of the network are monitored in real time to obtain the network bandwidth monitoring results; based on the network bandwidth monitoring results, the transmission parameter decision is made for the audio and video to be transmitted, and the first communication transmission scheme is generated. Then, the bandwidth characteristics of the network are predicted according to the network bandwidth monitoring results to obtain a network bandwidth prediction curve. Further, the multi-node communication quality impact prediction of the audio and video to be transmitted is predicted according to the network bandwidth prediction curve to obtain the multi-node communication quality impact prediction result. Next, the first communication transmission scheme is adaptively adjusted according to the multi-node communication quality impact prediction result to obtain the second communication transmission scheme. Finally, based on the network, the audio and video communication task is performed according to the second communication transmission scheme. The technical problem of unstable transmission quality of audio and video communication in the prior art when the network bandwidth fluctuates is solved, and the audio and video communication quality is optimized through adaptive bandwidth adjustment, thereby achieving the technical effect of improving the fluency and stability of the communication process. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0012] Figure 1A schematic diagram of a method for optimizing audio and video communication quality by adaptive bandwidth adjustment provided in an embodiment of the present application;

[0013] Figure 2 A schematic diagram of the structure of an audio and video communication quality optimization system with adaptive bandwidth adjustment provided in an embodiment of the present application.

[0014] Explanation of the reference numerals: task acquisition module 11 , network bandwidth monitoring module 12 , transmission parameter decision module 13 , network bandwidth prediction module 14 , communication quality prediction module 15 , adaptive adjustment module 16 , communication task execution module 17 . DETAILED DESCRIPTION

[0015] The present application solves the technical problem of unstable transmission quality of audio and video communication when network bandwidth fluctuates in the prior art by providing an audio and video communication quality optimization method and system with adaptive bandwidth adjustment.

[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, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0017] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or are inherent to these processes, methods, products or devices.

[0018] Embodiment 1, as Figure 1 As shown, the present application provides an audio and video communication quality optimization method with adaptive bandwidth adjustment, wherein the method includes:

[0019] An audio and video communication task of a network is obtained, wherein the audio and video communication task includes audio and video to be transmitted.

[0020] Obtain the audio and video transmission task that needs to be executed from the network. The audio and video transmission task includes the audio and video data to be transmitted and related information, such as the audio and video encoding format, data size, real-time requirements, and target receiving nodes. By clarifying the content and characteristics of the audio and video communication task, it can provide basic support for subsequent network bandwidth monitoring, transmission parameter decision-making, and optimization, so that the transmission plan can be designed according to the characteristics of the audio and video data, so as to better meet the bandwidth, delay, and transmission quality requirements of different tasks.

[0021] The bandwidth parameters of the network are monitored in real time to obtain network bandwidth monitoring results.

[0022] By monitoring the current operation status of the network, key parameter information related to network bandwidth can be obtained, such as bandwidth occupancy, delay, packet loss rate, jitter, etc. These parameters reflect the transmission capacity and fluctuation of the current network environment. By continuously collecting these bandwidth parameters, the network bandwidth monitoring results are obtained, which reflect the real-time transmission capacity of the current network.

[0023] Based on the network bandwidth monitoring result, a transmission parameter decision is made for the audio and video to be transmitted, and a first communication transmission plan is generated.

[0024] According to the real-time monitored network bandwidth conditions (such as bandwidth occupancy, delay, packet loss rate, etc.), analyze the transmission capacity that the network can currently support, and combine the characteristics of the audio and video to be transmitted (such as resolution, frame rate, audio encoding, etc.), select the appropriate encoding rate, frame rate, resolution, etc., and generate the first communication transmission plan to ensure that the audio and video data can be transmitted as efficiently as possible under the current network conditions.

[0025] Furthermore, based on the network bandwidth monitoring result, a transmission parameter decision is made for the audio and video to be transmitted, and a first communication transmission scheme is generated, including:

[0026] Collect the basic information of the audio and video to be transmitted to obtain the audio and video information; perform integrated fusion learning based on K communication transmission decision learners to establish a communication transmission decision channel, where K is a positive integer greater than 1; input the network bandwidth monitoring result and the audio and video information into the communication transmission decision channel to generate the first communication transmission plan.

[0027] Specifically, basic information of the audio and video to be transmitted is collected, including the resolution, frame rate, encoding format, data size and real-time requirements of the audio and video, to form audio and video information. This information is the basis for making transmission parameter decisions and is also an important input for subsequent integrated fusion learning; K communication transmission decision learners are initialized and called (K is a positive integer greater than 1), where each communication transmission decision learner independently analyzes the transmission parameters based on a machine learning model (such as a decision tree model, a random forest model, a support vector machine model, a deep neural network model or a gradient boosting decision tree model, etc.), and generates a sub-plan based on its historical decision-making experience and real-time network status. For example, the decision tree model quickly generates recommended values ​​​​of transmission parameters according to bandwidth status and audio and video characteristics by constructing a hierarchical decision path; the deep neural network learns the complex nonlinear relationship between bandwidth monitoring results and audio and video information by constructing a multi-layer nonlinear network structure, thereby generating accurate transmission parameter decisions. By integrating fusion learning methods (such as weighted average, voting mechanism or fusion strategy based on Bayesian theory), the decision results of K communication transmission decision learners are comprehensively analyzed, and a communication transmission decision channel is established to achieve multi-dimensional intelligent optimization of audio and video transmission parameters; the real-time network bandwidth monitoring results and the collected audio and video information are used as input and input into the communication transmission decision channel. After fusion calculation and dynamic optimization, a preliminary transmission strategy is output to generate the first communication transmission plan, which includes parameter configurations such as encoding format selection, resolution adjustment, frame rate optimization, packet subpackaging strategy and transmission path optimization, providing basic solution support for the subsequent transmission of audio and video data that adapts to the current network status.

[0028] Furthermore, integrated fusion learning is performed based on K communication transmission decision learners to establish a communication transmission decision channel, including:

[0029] A network bandwidth monitoring sample set, an audio and video information sample set and a communication transmission scheme sample set are obtained; the K communication transmission decision learners are supervised and trained according to the network bandwidth monitoring sample set, the audio and video information sample set and the communication transmission scheme sample set to obtain K communication transmission decision models; the output data sets of the K communication transmission decision models are used as input information and the communication transmission scheme sample set is used as output information to train a communication transmission decision fusion model; the K communication transmission decision models are used as the first-level communication transmission decision nodes and the communication transmission decision fusion model is used as the second-level communication transmission decision node; the first-level communication transmission decision node and the second-level communication transmission decision node are connected to generate the communication transmission decision channel.

[0030] Specifically, the network operation and communication history data are collected to construct a network bandwidth monitoring sample set, an audio and video information sample set, and a communication transmission plan sample set. The network bandwidth monitoring sample set includes network parameters such as bandwidth occupancy, delay, packet loss rate, and jitter, and their time series data; the audio and video information sample set includes characteristics such as resolution, frame rate, encoding format, and data size of audio and video; and the communication transmission plan sample set includes transmission parameter configurations generated for different network and audio and video states in history, such as encoding schemes, resolution adjustment, and packetization strategies. K communication transmission decision learners are supervised and trained, where K is a positive integer greater than 1. During the training process, the network bandwidth monitoring sample set and the audio and video information sample set are used as inputs, and the communication transmission plan sample set is used as the target output. A supervised learning algorithm (such as gradient descent or cross entropy loss optimization) is used to train each learner to generate K communication transmission decision models. These models capture the relationship between network bandwidth characteristics and audio and video parameters, and independently generate optimization suggestions for transmission parameters. Based on the K communication transmission decision models obtained through training, a communication transmission decision fusion model is constructed. Specifically, the output data sets of the K models are taken as input, and the communication transmission scheme sample set is taken as the target output to train the fusion model. The fusion model uses ensemble learning methods such as stacking or weighted averaging to optimize and integrate the prediction results of multiple models to generate more accurate and robust transmission parameter configuration results.

[0031] After completing the above model training, the node structure of the communication transmission decision channel is constructed. First, K communication transmission decision models are set as the first-level nodes of the communication transmission decision. These nodes are responsible for receiving the network bandwidth monitoring results and audio and video information, independently processing and generating preliminary transmission parameter recommendations; then, the communication transmission decision fusion model is set as the second-level node of the communication transmission decision, receiving the output of the first-level node of the communication transmission decision, and generating the final optimized transmission plan through integrated calculation. Finally, the first-level node of the communication transmission decision and the second-level node of the communication transmission decision are connected through data streams to form a complete communication transmission decision channel. The communication transmission decision channel dynamically generates the optimal communication transmission plan for the current network status by inputting the network bandwidth monitoring results and audio and video information in real time, and through the distributed processing of the first-level node and the integrated optimization of the second-level node, providing intelligent support for the efficient transmission of audio and video data.

[0032] The bandwidth characteristics of the network are predicted according to the network bandwidth monitoring result to obtain a network bandwidth prediction curve.

[0033] First, collect time series data from real-time network bandwidth monitoring results, including parameters such as bandwidth occupancy, latency, packet loss rate, and jitter, and preprocess the data, including denoising (such as using the moving average method to smooth sudden fluctuations), filling missing values ​​(such as filling missing data through linear interpolation), and normalization, adjust the data range to a unified scale to ensure the accuracy and efficiency of subsequent model training. Then, extract key features from the preprocessed data, including time series features (such as short-term trends and periodic fluctuations), statistical features (such as mean, standard deviation, rate of change), and derived features (such as the growth rate and fluctuation amplitude of bandwidth occupancy), and generate feature vectors for prediction. Then, based on the extracted features, construct a bandwidth prediction model suitable for time series prediction. Model selection can include classical statistical models (such as ARIMA or SARIMA), machine learning models (such as support vector regression SVR or gradient boosting regression tree GBRT), and deep learning models (such as long short-term memory network LSTM or Transformer-based time series model). By using historical bandwidth data to supervise the model training, taking the past network bandwidth change characteristics as input, and taking the corresponding future bandwidth change trend as the target output, the model parameters are optimized to generate a bandwidth prediction model that can accurately predict the network bandwidth characteristics. After the model training is completed, the real-time network bandwidth monitoring results are input into the trained bandwidth prediction model, and the model is used to predict the bandwidth change trend in the future. The prediction results include key parameters such as bandwidth occupancy, latency, and jitter at different time points in the future. Finally, the prediction results are converted into a visual curve of bandwidth changes over time, namely the network bandwidth prediction curve. This curve clearly shows the future change trend of network bandwidth, marks possible peak periods, fluctuation ranges, and stable intervals, and provides intuitive decision-making basis and reference for subsequent communication transmission optimization.

[0034] The multi-node communication quality impact prediction is performed on the audio and video to be transmitted according to the network bandwidth prediction curve to obtain the multi-node communication quality impact prediction result.

[0035] Based on the network bandwidth prediction curve, the communication quality of the audio and video to be transmitted on multiple transmission nodes is further predicted, including the prediction of delays, packet loss and other problems that may be encountered by each node, as well as the specific impact of these problems on the audio and video quality, thereby obtaining the prediction results of the impact on multi-node communication quality.

[0036] Furthermore, the multi-node communication quality impact prediction is performed on the audio and video to be transmitted according to the network bandwidth prediction curve to obtain the multi-node communication quality impact prediction result, including:

[0037] According to the network bandwidth prediction curve, a change trend evaluation is performed to obtain a bandwidth change trend evaluation result; according to the bandwidth change trend evaluation result, the network bandwidth prediction curve is divided to obtain a multi-node bandwidth prediction result; according to the multi-node bandwidth prediction result, a bandwidth adequacy evaluation is performed to obtain a multi-node bandwidth adequacy; based on the multi-node bandwidth adequacy, the communication quality impact of the audio and video to be transmitted is predicted according to the multi-node bandwidth prediction result to generate the multi-node communication quality impact prediction result.

[0038] Specifically, the bandwidth change trend is evaluated according to the network bandwidth prediction curve, and the bandwidth change trend evaluation results are obtained by analyzing the bandwidth change amplitude, fluctuation period, trend direction, etc., which are used to quantify the future fluctuation characteristics and stability of the network; the network bandwidth prediction curve is segmented according to the bandwidth change trend evaluation results, and divided into bandwidth prediction sub-intervals of different nodes, so as to obtain multi-node bandwidth prediction results corresponding to each node, which reflect the changes in bandwidth resources of each node in the future; the bandwidth adequacy of each node is evaluated according to the multi-node bandwidth prediction results, and the bandwidth adequacy of each node is calculated. By comparing the available bandwidth of the node with the audio and video transmission requirements (such as bandwidth occupancy, real-time requirements, etc.), it is evaluated whether the node has sufficient transmission capacity; based on the bandwidth adequacy of each node and the multi-node bandwidth prediction results, combined with the characteristics of audio and video (such as resolution, frame rate, encoding format, etc.), the multi-node communication quality impact prediction of the transmitted audio and video is predicted, and the multi-node communication quality impact prediction results are generated by evaluating indicators such as delay, packet loss rate, and jitter, and the quality score of each node and potential bottleneck nodes are clarified, providing accurate data support for subsequent communication parameter optimization.

[0039] Furthermore, based on the multi-node bandwidth margin, the communication quality impact of the audio and video to be transmitted is predicted according to the multi-node bandwidth prediction result, and the multi-node communication quality impact prediction result is generated, including:

[0040] According to the multi-node bandwidth margin and the multi-node bandwidth prediction result, extract the n-th node bandwidth margin and the n-th node bandwidth prediction result, wherein n is a positive integer; determine whether the n-th node bandwidth margin is less than the predetermined bandwidth margin; if the n-th node bandwidth margin is less than the predetermined bandwidth margin, perform a multi-dimensional loss prediction of the communication quality of the audio and video to be transmitted according to the n-th node bandwidth prediction result, and generate an n-th node communication quality impact prediction result; add the n-node communication quality impact prediction result to the multi-node communication quality impact prediction result.

[0041] Specifically, the bandwidth margin and bandwidth prediction result of the nth node are extracted from the multi-node bandwidth margin and multi-node bandwidth prediction results, where n is a positive integer, and are used to evaluate each node in turn; it is determined whether the bandwidth margin of the nth node is less than a predetermined bandwidth margin threshold, and if the bandwidth margin is greater than or equal to the threshold, the detailed evaluation of the node is skipped; if the bandwidth margin is less than the threshold, a multi-dimensional loss prediction of the communication quality of the nth node is performed based on the bandwidth prediction result of the nth node and the characteristics of the audio and video to be transmitted (such as resolution, frame rate and real-time requirements), including evaluating possible delays, packet loss rates and jitters, quantifying their potential impact on the audio and video transmission quality, and generating the communication quality impact prediction result of the nth node; the prediction result of the nth node is added to the overall multi-node communication quality impact prediction result list, and the same process is continued for other nodes until all nodes are evaluated, and finally a comprehensive multi-node communication quality impact prediction result is generated, providing a detailed quality analysis and evaluation basis for subsequent communication optimization and node selection.

[0042] Furthermore, a multi-dimensional loss prediction of the communication quality of the audio and video to be transmitted is performed according to the n-th node bandwidth prediction result to generate the n-th node communication quality impact prediction result, including:

[0043] According to the nth node bandwidth prediction result, the transmission clarity loss of the audio and video to be transmitted is predicted to obtain the nth node transmission clarity loss prediction result; according to the nth node bandwidth prediction result, the transmission fluency loss of the audio and video to be transmitted is predicted to generate the nth node transmission fluency loss prediction result; according to the nth node bandwidth prediction result, the transmission efficiency loss of the audio and video to be transmitted is predicted to generate the nth node transmission efficiency loss prediction result; the nth node transmission clarity loss prediction result, the nth node transmission fluency loss prediction result and the nth node transmission efficiency loss prediction result are sorted to generate the nth node communication quality impact prediction result.

[0044] Specifically, according to the bandwidth prediction result of the nth node, combined with the characteristics of the resolution, frame rate and encoding format of the audio and video to be transmitted, the transmission clarity loss of the audio and video at the node is predicted, and the resolution degradation or image blurring caused by insufficient bandwidth is analyzed to generate the transmission clarity loss prediction result of the nth node; then, according to the bandwidth prediction result of the nth node, the smoothness loss of audio and video transmission is predicted, and the transmission smoothness loss prediction result of the nth node is generated by evaluating the possible delay, jitter and frame rate degradation; then, based on the bandwidth prediction result of the nth node, combined with the real-time requirements of audio and video, the transmission efficiency loss is calculated, including factors such as reduced transmission speed and data packet loss, to generate the transmission efficiency loss prediction result of the nth node; finally, the above-mentioned nth node transmission clarity loss prediction result, nth node transmission smoothness loss prediction result and nth node transmission efficiency loss prediction result are sorted and integrated to generate the communication quality impact prediction result of the nth node as a comprehensive evaluation data of the impact of this node on the audio and video transmission quality.

[0045] Furthermore, if the bandwidth margin of the nth node is greater than or equal to the predetermined bandwidth margin, a multi-dimensional incentive prediction of the communication quality of the audio and video to be transmitted is performed based on the nth node bandwidth prediction result to generate the nth node communication quality impact prediction result.

[0046] Specifically, according to the bandwidth prediction result of the nth node, combined with the characteristics of the audio and video to be transmitted (such as resolution, frame rate and real-time requirements), the transmission clarity improvement of the audio and video at the node is predicted, and by analyzing the resource redundancy under the condition of sufficient bandwidth, the higher resolution or better encoding mode that can be supported is predicted, and the transmission clarity incentive prediction result of the nth node is generated; then, according to the bandwidth prediction result of the nth node, its optimization effect on the smoothness of audio and video transmission is evaluated, and the potential for possible delay reduction, jitter reduction and frame rate improvement during smooth playback is analyzed to generate the transmission smoothness incentive prediction result of the nth node; then, according to the bandwidth prediction result of the nth node, combined with the efficiency requirements of the audio and video transmission task, the possible transmission speed improvement and data transmission integrity enhancement under sufficient bandwidth are evaluated to generate the transmission efficiency incentive prediction result of the nth node; finally, the above-mentioned nth node transmission clarity incentive prediction result, nth node transmission smoothness incentive prediction result and nth node transmission efficiency incentive prediction result are sorted and integrated to generate the communication quality impact prediction result of the nth node, which serves as a comprehensive evaluation of the optimization of audio and video transmission quality of the node under the condition of sufficient bandwidth, and provides support for subsequent transmission parameter optimization.

[0047] The first communication transmission scheme is adaptively adjusted according to the multi-node communication quality impact prediction result to obtain a second communication transmission scheme.

[0048] From the prediction results of multi-node communication quality impact, extract the key quality parameters of each node, including transmission clarity loss / incentive, smoothness loss / incentive, and efficiency loss / incentive, and mark the nodes with insufficient bandwidth, nodes with sufficient bandwidth, and critical nodes; match and analyze the transmission parameters of the first communication transmission scheme (such as resolution, frame rate, encoding format, packet subdivision strategy, etc.) with the prediction results of multi-node communication quality impact, and evaluate the adaptability of the first scheme under the current network status. For nodes marked as insufficient bandwidth, dynamically adjust the transmission parameters, reduce bandwidth occupancy by reducing resolution, frame rate, or switching to a more efficient encoding format, and adjust the transmission path to bypass high-loss nodes when necessary; for nodes with sufficient bandwidth, make full use of their network resources to improve audio and video clarity and smoothness, such as increasing resolution and frame rate or choosing a higher-quality encoding method; for critical nodes, set up a dynamic adjustment mechanism, such as optimizing the packet subdivision strategy or adjusting the buffer size, to adapt to possible fluctuations in bandwidth. The adjusted node transmission parameters are comprehensively used to generate the second communication transmission plan, which includes the optimized parameter configuration of each node (such as resolution, frame rate, encoding format, packet subpackaging strategy, etc.) and the global transmission path and priority configuration, to ensure that the second communication transmission plan can achieve better transmission quality and resource utilization efficiency under the current network conditions.

[0049] Based on the network, the audio and video communication task is performed according to the second communication transmission scheme.

[0050] According to the adjusted communication transmission plan 2, the audio and video communication tasks are actually completed. By executing the dynamically optimized communication transmission plan 2, the quality and stability of audio and video transmission can be improved, and the problems of freezing, delay and data loss can be reduced.

[0051] Furthermore, based on the network, performing the audio and video communication task according to the second communication transmission scheme also includes:

[0052] Obtaining an updated result of network bandwidth monitoring; evaluating the degree of change of the updated result of network bandwidth monitoring based on the network bandwidth prediction curve to obtain the degree of bandwidth change; determining whether the degree of bandwidth change is greater than or equal to a predetermined degree of bandwidth change; if the degree of bandwidth change is greater than or equal to the predetermined degree of bandwidth change, adjusting the second communication transmission scheme in real time according to the updated result of network bandwidth monitoring.

[0053] When performing audio and video communication tasks, the network bandwidth monitoring update results are obtained in real time, including key parameters such as the bandwidth occupancy rate, delay, packet loss rate and jitter of the current network; then, based on the network bandwidth prediction curve, the latest network bandwidth monitoring update results are evaluated for the degree of change, and the bandwidth change degree is calculated by analyzing the deviation between the current monitoring data and the prediction curve to quantify the degree of network state fluctuation. Next, it is determined whether the bandwidth change degree is greater than or equal to the predetermined bandwidth change degree threshold. If the bandwidth change degree does not reach the predetermined threshold, the second communication transmission scheme is kept unchanged, and the audio and video communication tasks are continued to be performed according to the original scheme; if the bandwidth change degree is greater than or equal to the predetermined threshold, it means that the current network state has fluctuated significantly. At this time, according to the latest network bandwidth monitoring update results, the second communication transmission scheme is adjusted in real time. The adjustment content includes: reducing the resolution or frame rate for nodes with reduced bandwidth, switching to a more efficient encoding format or backup transmission path; improving the audio and video clarity for nodes with increased bandwidth, such as increasing the resolution and frame rate, and making full use of the newly added network resources. The adjusted second communication transmission plan will take effect across the entire network, ensuring that audio and video communication tasks can adapt to changes in the dynamic network environment, maintain the clarity, fluency and efficiency of data transmission, and ultimately achieve high quality and stability of audio and video transmission. Through this real-time adjustment mechanism, it can effectively cope with fluctuations in complex network environments and provide continuously optimized transmission guarantees for audio and video communication tasks.

[0054] In summary, the embodiments of the present application have at least the following technical effects:

[0055] First, the audio and video communication task of the network is obtained, wherein the audio and video communication task includes the audio and video to be transmitted. Next, the bandwidth parameters of the network are monitored in real time to obtain the network bandwidth monitoring results; based on the network bandwidth monitoring results, the transmission parameter decision is made for the audio and video to be transmitted, and the first communication transmission scheme is generated. Then, the bandwidth characteristics of the network are predicted according to the network bandwidth monitoring results to obtain a network bandwidth prediction curve. Further, the multi-node communication quality impact prediction of the audio and video to be transmitted is predicted according to the network bandwidth prediction curve to obtain the multi-node communication quality impact prediction result. Next, the first communication transmission scheme is adaptively adjusted according to the multi-node communication quality impact prediction result to obtain the second communication transmission scheme. Finally, based on the network, the audio and video communication task is performed according to the second communication transmission scheme. The technical problem of unstable transmission quality of audio and video communication in the prior art when the network bandwidth fluctuates is solved, and the audio and video communication quality is optimized through adaptive bandwidth adjustment, thereby achieving the technical effect of improving the fluency and stability of the communication process.

[0056] Embodiment 2, based on the same inventive concept as the method for optimizing the quality of audio and video communication by adaptive bandwidth adjustment in the above-mentioned embodiment, Figure 2As shown, the present application provides an audio and video communication quality optimization system with adaptive bandwidth adjustment, wherein the system includes:

[0057] Task acquisition module 11: obtains the audio and video communication task of the network, wherein the audio and video communication task includes the audio and video to be transmitted; network bandwidth monitoring module 12: monitors the bandwidth parameters of the network in real time to obtain the network bandwidth monitoring result; transmission parameter decision module 13: makes transmission parameter decision for the audio and video to be transmitted based on the network bandwidth monitoring result, and generates a first communication transmission scheme; network bandwidth prediction module 14: predicts the bandwidth characteristics of the network according to the network bandwidth monitoring result, and obtains a network bandwidth prediction curve; communication quality prediction module 15: predicts the multi-node communication quality impact of the audio and video to be transmitted according to the network bandwidth prediction curve, and obtains a multi-node communication quality impact prediction result; adaptive adjustment module 16: adaptively adjusts the first communication transmission scheme according to the multi-node communication quality impact prediction result, and obtains a second communication transmission scheme; communication task execution module 17: executes the audio and video communication task according to the second communication transmission scheme based on the network.

[0058] Furthermore, the transmission parameter decision module 13 is used to execute the following method:

[0059] Collect the basic information of the audio and video to be transmitted to obtain the audio and video information; perform integrated fusion learning based on K communication transmission decision learners to establish a communication transmission decision channel, where K is a positive integer greater than 1; input the network bandwidth monitoring result and the audio and video information into the communication transmission decision channel to generate the first communication transmission plan.

[0060] Furthermore, the transmission parameter decision module 13 is used to execute the following method:

[0061] A network bandwidth monitoring sample set, an audio and video information sample set and a communication transmission scheme sample set are obtained; the K communication transmission decision learners are supervised and trained according to the network bandwidth monitoring sample set, the audio and video information sample set and the communication transmission scheme sample set to obtain K communication transmission decision models; the output data sets of the K communication transmission decision models are used as input information and the communication transmission scheme sample set is used as output information to train a communication transmission decision fusion model; the K communication transmission decision models are used as the first-level communication transmission decision nodes and the communication transmission decision fusion model is used as the second-level communication transmission decision node; the first-level communication transmission decision node and the second-level communication transmission decision node are connected to generate the communication transmission decision channel.

[0062] Furthermore, the communication quality prediction module 15 is used to perform the following method:

[0063] According to the network bandwidth prediction curve, a change trend evaluation is performed to obtain a bandwidth change trend evaluation result; according to the bandwidth change trend evaluation result, the network bandwidth prediction curve is divided to obtain a multi-node bandwidth prediction result; according to the multi-node bandwidth prediction result, a bandwidth adequacy evaluation is performed to obtain a multi-node bandwidth adequacy; based on the multi-node bandwidth adequacy, the communication quality impact of the audio and video to be transmitted is predicted according to the multi-node bandwidth prediction result to generate the multi-node communication quality impact prediction result.

[0064] Furthermore, the communication quality prediction module 15 is used to perform the following method:

[0065] According to the multi-node bandwidth margin and the multi-node bandwidth prediction result, extract the n-th node bandwidth margin and the n-th node bandwidth prediction result, wherein n is a positive integer; determine whether the n-th node bandwidth margin is less than the predetermined bandwidth margin; if the n-th node bandwidth margin is less than the predetermined bandwidth margin, perform a multi-dimensional loss prediction of the communication quality of the audio and video to be transmitted according to the n-th node bandwidth prediction result, and generate an n-th node communication quality impact prediction result; add the n-node communication quality impact prediction result to the multi-node communication quality impact prediction result.

[0066] Furthermore, the communication quality prediction module 15 is used to perform the following method:

[0067] According to the nth node bandwidth prediction result, the transmission clarity loss of the audio and video to be transmitted is predicted to obtain the nth node transmission clarity loss prediction result; according to the nth node bandwidth prediction result, the transmission fluency loss of the audio and video to be transmitted is predicted to generate the nth node transmission fluency loss prediction result; according to the nth node bandwidth prediction result, the transmission efficiency loss of the audio and video to be transmitted is predicted to generate the nth node transmission efficiency loss prediction result; the nth node transmission clarity loss prediction result, the nth node transmission fluency loss prediction result and the nth node transmission efficiency loss prediction result are sorted to generate the nth node communication quality impact prediction result.

[0068] Furthermore, the communication quality prediction module 15 is used to perform the following method:

[0069] If the bandwidth margin of the nth node is greater than or equal to the predetermined bandwidth margin, a multi-dimensional incentive prediction of the communication quality of the audio and video to be transmitted is performed according to the nth node bandwidth prediction result to generate the nth node communication quality impact prediction result.

[0070] Furthermore, the communication task execution module 17 is used to execute the following method:

[0071] Obtaining an updated result of network bandwidth monitoring; evaluating the degree of change of the updated result of network bandwidth monitoring based on the network bandwidth prediction curve to obtain the degree of bandwidth change; determining whether the degree of bandwidth change is greater than or equal to a predetermined degree of bandwidth change; if the degree of bandwidth change is greater than or equal to the predetermined degree of bandwidth change, adjusting the second communication transmission scheme in real time according to the updated result of network bandwidth monitoring.

[0072] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0073] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0074] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A method for optimizing audio and video communication quality with adaptive bandwidth adjustment, characterized in that: The method comprises: Obtaining an audio and video communication task of a network, wherein the audio and video communication task includes audio and video to be transmitted; Monitor the bandwidth parameters of the network in real time to obtain network bandwidth monitoring results; Based on the network bandwidth monitoring result, a transmission parameter decision is made for the audio and video to be transmitted, and a first communication transmission plan is generated; Predicting bandwidth characteristics of the network according to the network bandwidth monitoring result to obtain a network bandwidth prediction curve; According to the network bandwidth prediction curve, a multi-node communication quality impact prediction is performed on the audio and video to be transmitted to obtain a multi-node communication quality impact prediction result; Adaptively adjusting the first communication transmission scheme according to the multi-node communication quality impact prediction result to obtain a second communication transmission scheme; Based on the network, the audio and video communication task is performed according to the second communication transmission scheme.

2. The method for optimizing audio and video communication quality by adaptive bandwidth adjustment according to claim 1, characterized in that: Based on the network bandwidth monitoring result, a transmission parameter decision is made for the audio and video to be transmitted, and a first communication transmission scheme is generated, including: Collecting basic information of the audio and video to be transmitted to obtain audio and video information; Perform integrated fusion learning based on K communication transmission decision learners to establish a communication transmission decision channel, where K is a positive integer greater than 1; The network bandwidth monitoring result and the audio and video information are input into the communication transmission decision channel to generate the first communication transmission plan.

3. The method for optimizing audio and video communication quality by adaptive bandwidth adjustment according to claim 2, characterized in that: Based on K communication transmission decision learners, integrated fusion learning is performed to establish a communication transmission decision channel, including: Obtain network bandwidth monitoring sample sets, audio and video information sample sets, and communication transmission solution sample sets; Performing supervised training on the K communication transmission decision learners according to the network bandwidth monitoring sample set, the audio and video information sample set, and the communication transmission scheme sample set to obtain K communication transmission decision models; Using the output data sets of the K communication transmission decision models as input information and the communication transmission scheme sample set as output information, training the communication transmission decision fusion model; The K communication transmission decision models are used as the communication transmission decision primary nodes, and the communication transmission decision fusion model is used as the communication transmission decision secondary node; The communication transmission decision-making primary node and the communication transmission decision-making secondary node are connected to generate the communication transmission decision-making channel.

4. The method for optimizing audio and video communication quality by adaptive bandwidth adjustment according to claim 1, characterized in that: Predicting the impact of multi-node communication quality on the audio and video to be transmitted according to the network bandwidth prediction curve to obtain a multi-node communication quality impact prediction result includes: Performing a change trend evaluation according to the network bandwidth prediction curve to obtain a bandwidth change trend evaluation result; Divide the network bandwidth prediction curve according to the bandwidth change trend evaluation result to obtain a multi-node bandwidth prediction result; Performing bandwidth adequacy evaluation according to the multi-node bandwidth prediction results to obtain multi-node bandwidth adequacy; Based on the multi-node bandwidth margin, the communication quality impact of the audio and video to be transmitted is predicted according to the multi-node bandwidth prediction result to generate the multi-node communication quality impact prediction result.

5. The method for optimizing audio and video communication quality by adaptive bandwidth adjustment according to claim 4, characterized in that: Based on the multi-node bandwidth margin, predicting the communication quality impact of the audio and video to be transmitted according to the multi-node bandwidth prediction result, and generating the multi-node communication quality impact prediction result, including: Extracting the bandwidth margin of the nth node and the bandwidth prediction result of the nth node according to the multi-node bandwidth margin and the multi-node bandwidth prediction result, wherein n is a positive integer; Determining whether the bandwidth margin of the nth node is less than a predetermined bandwidth margin; If the bandwidth margin of the nth node is less than the predetermined bandwidth margin, a multi-dimensional loss prediction of the communication quality of the audio and video to be transmitted is performed according to the nth node bandwidth prediction result to generate a prediction result of the communication quality impact of the nth node; The n-th node communication quality impact prediction result is added to the multi-node communication quality impact prediction results.

6. The method for optimizing audio and video communication quality by adaptive bandwidth adjustment according to claim 5, characterized in that: The multi-dimensional loss prediction of the communication quality of the audio and video to be transmitted is performed according to the bandwidth prediction result of the nth node, and the communication quality impact prediction result of the nth node is generated, including: Predicting the transmission clarity loss of the audio and video to be transmitted according to the n-th node bandwidth prediction result to obtain the n-th node transmission clarity loss prediction result; According to the n-th node bandwidth prediction result, the transmission smoothness loss of the audio and video to be transmitted is predicted, and the n-th node transmission smoothness loss prediction result is generated; According to the n-th node bandwidth prediction result, the transmission efficiency loss of the audio and video to be transmitted is predicted, and the n-th node transmission efficiency loss prediction result is generated; Arrange the prediction result of the transmission clarity loss of the n-th node, the prediction result of the transmission fluency loss of the n-th node and the prediction result of the transmission efficiency loss of the n-th node to generate the prediction result of the communication quality impact of the n-th node.

7. The method for optimizing audio and video communication quality by adaptive bandwidth adjustment according to claim 5, characterized in that: If the bandwidth margin of the nth node is greater than or equal to the predetermined bandwidth margin, a multi-dimensional incentive prediction of the communication quality of the audio and video to be transmitted is performed according to the nth node bandwidth prediction result to generate the nth node communication quality impact prediction result.

8. The method for optimizing audio and video communication quality by adaptive bandwidth adjustment according to claim 1, characterized in that: Based on the network, performing the audio and video communication task according to the second communication transmission scheme also includes: Get updated results of network bandwidth monitoring; Based on the network bandwidth prediction curve, the network bandwidth monitoring update result is evaluated for a degree of change to obtain a bandwidth change degree; Determining whether the bandwidth variation is greater than or equal to a predetermined bandwidth variation; If the bandwidth variation degree is greater than or equal to the predetermined bandwidth variation degree, the second communication transmission scheme is adjusted in real time according to the network bandwidth monitoring update result.

9. An audio and video communication quality optimization system with adaptive bandwidth adjustment, characterized in that: The method for optimizing audio and video communication quality by adaptive bandwidth adjustment according to any one of claims 1 to 8, the system comprising: Task acquisition module: obtains the audio and video communication task of the network, wherein the audio and video communication task includes the audio and video to be transmitted; Network bandwidth monitoring module: monitors the bandwidth parameters of the network in real time and obtains network bandwidth monitoring results; Transmission parameter decision module: based on the network bandwidth monitoring result, the transmission parameter decision is made for the audio and video to be transmitted, and a first communication transmission plan is generated; Network bandwidth prediction module: predicts bandwidth characteristics of the network according to the network bandwidth monitoring result to obtain a network bandwidth prediction curve; Communication quality prediction module: predicting the impact of multi-node communication quality on the audio and video to be transmitted according to the network bandwidth prediction curve, and obtaining the multi-node communication quality impact prediction result; Adaptive adjustment module: adaptively adjust the first communication transmission scheme according to the multi-node communication quality impact prediction result to obtain a second communication transmission scheme; Communication task execution module: based on the network, executes the audio and video communication task according to the second communication transmission scheme.

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