Survey on reinforcement learning based adaptive bit rate algorithm for mobile video streaming services - Li’na DU; Li ZHUO; Shuo YANG; Jiafeng LI; Jing ZHANG (Tongxin xuebao)
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.