QoE-Fair DASH Video Streaming Using Server-side Reinforcement Learning
QoE-Fair DASH Video Streaming Using Server-side Reinforcement Learning
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DOI：
10.1145/3397227
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发表时间：
2020-07-01
影响因子：
5.1
通讯作者：
Shirmohammadi, Shervin
中科院分区：
文献类型：
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作者：
Altamimi, Sa'di;Shirmohammadi, Shervin
为了设计最优的自适应视频流方法，视频服务提供商需要同时考虑其用户的体验质量（QoE）的效率和公平性。在参考文献[8]中，我们提出了一种服务器侧QoE公平速率自适应方法，该方法考虑了QoE的效率和公平性。服务器使用强化学习（RL）来以在并发DASH客户端之间实现公平的方式为共享到服务器的相同瓶颈链路的每个客户端选择比特率，并且通过动态修改客户端的媒体呈现描述（MPD）文件来强加该比特率。在本文中，我们扩展了这项工作，以最大限度地减少服务器需要采取的操作数量，以保持系统处于平衡状态。通过结合递归神经网络，特别是LSTM模型，我们修改了服务器的训练算法，以提高服务器引导客户端的操作的质量和数量。性能评估的客户端运行同构和异构的自适应算法的修改后的算法表明，服务器动作的数量分别下降了14%和22%，而QoE的公平性提高了至少6%和10%，分别。
To design an optimal adaptive video streaming method, video service providers need to consider both the efficiency and the fairness of the Quality of Experience (QoE) of their users. In Reference [8], we proposed a server-side QoE-fair rate adaptation method that considers both efficiency and fairness of the QoE. The server uses Reinforcement Learning (RL) to select a bitrate for each client sharing the same bottleneck link to the server in a way that achieves fairness among concurrent DASH clients and imposes that bitrate by dynamically modifying the client's Media Presentation Description (MPD) file. In this article, we extend that work to minimize the number of actions the server needs to take to keep the system in its equilibrium state. By incorporating a Recurrent Neural Network, specifically an LSTM model, we modify the server's training algorithm to achieve improvements in both the quality and the quantity of actions the server takes to guide the client. Performance evaluation of the modified algorithm for clients running both homogeneous and heterogeneous adaptation algorithms showed that the number of server actions dropped by 14% and 22%, respectively, while QoE-fairness improved by at least 6% and 10%, respectively.