基于强化学习的移动视频流业务码率自适应算法研究进展

Survey on reinforcement learning based adaptive bit rate algorithm for mobile video streaming services

近几年来,随着HTTP自适应流媒体(HAS)视频数据集和网络轨迹数据集的不断推出,强化学习、深度学习等机器学习方法被不断应用到码率自适应(ABR)算法中,通过交互学习来确定码率控制的最优策略,取得了远超过传统启发式方法的性能。在分析ABR算法研究难点的基础上,重点阐述了基于强化学习(包括深度强化学习)的ABR算法研究进展。此外,总结了代表性的HAS视频数据集和网络轨迹数据集,介绍了算法性能的评价准则,最后探讨了ABR研究目前存在的问题和未来的方向。

In recent years,with the continuous release of HTTP adaptive streaming(HAS)video datasets and network trace datasets,the machine learning methods,such as deep learning and reinforcement learning,have been continuously applied to adaptive bit rate(ABR)algorithms,which obtain the optimal strategy of rate control through interactive learning,and achieve superior performance that surpasses the traditional heuristic methods.Based on the analysis of the research difficulties of ABR algorithms,the research advances of ABR algorithms based on reinforcement learning(including deep reinforcement learning)was investigated.Furthermore,several representative HAS video datasets and network trace datasets were summarized,the evaluation metrics of the performance were depicted.Finally,the existing problems and the future tendency of ABR research were discussed.