Kim Yu-Sin, Lee Jeong-Min, Ryu Jong-Yeol, Ban Tae-Won
Algorithm Team, Carvi, Seoul 08513, Korea.
Department of Information and Communication Engineering, Gyeongsang National University, Gyeongnam 53064, Korea.
Sensors (Basel). 2021 Apr 19;21(8):2867. doi: 10.3390/s21082867.
As the demand for video streaming has been rapidly increasing recently, new technologies for improving the efficiency of video streaming have attracted much attention. In this paper, we thus investigate how to improve the efficiency of video streaming by using clients' cache storage considering exclusive OR (XOR) coding-based video streaming where multiple different video contents can be simultaneously transmitted in one transmission as long as prerequisite conditions are satisfied, and the efficiency of video streaming can be thus significantly enhanced. We also propose a new cache update scheme using reinforcement learning. The proposed scheme uses a -actor-critic (-AC) network that can mitigate the disadvantage of actor-critic networks by yielding candidate outputs and by selecting the final output with the highest value out of the candidates. The -AC exists in each client, and each client can train it by using only locally available information without any feedback or signaling so that the proposed cache update scheme is a completely decentralized scheme. The performance of the proposed cache update scheme was analyzed in terms of the average number of transmissions for XOR coding-based video streaming and was compared to that of conventional cache update schemes. Our numerical results show that the proposed cache update scheme can reduce the number of transmissions up to 24% when the number of videos is 100, the number of clients is 50, and the cache size is 5.
近年来，随着视频流需求的迅速增长，提高视频流效率的新技术备受关注。因此，在本文中，我们研究如何通过利用客户端缓存存储来提高视频流的效率，考虑基于异或（XOR）编码的视频流，在满足前提条件的情况下，多个不同的视频内容可以在一次传输中同时传输，从而显著提高视频流的效率。我们还提出了一种使用强化学习的新缓存更新方案。所提出的方案使用一种α-演员-评论家（α-AC）网络，该网络可以通过产生多个候选输出并从这些候选输出中选择具有最高值的最终输出，来减轻演员-评论家网络的缺点。α-AC存在于每个客户端中，每个客户端可以仅使用本地可用信息对其进行训练，而无需任何反馈或信令，因此所提出的缓存更新方案是一种完全分散的方案。根据基于异或编码的视频流的平均传输次数，分析了所提出的缓存更新方案的性能，并与传统缓存更新方案的性能进行了比较。我们的数值结果表明，当视频数量为100、客户端数量为50且缓存大小为5时，所提出的缓存更新方案可以将传输次数减少多达24%。
