QoE-Based Server Selection for Mobile Video Streaming

被引：3

作者：

Tapang, Daniel Kanba ^{[1
]}

Huang, Siqi ^{[2
]}

Huang, Xueqing ^{[1
]}

机构：

[1] New York Inst Technol, Dept Comp Sci, Old Westbury, NY 11568 USA

[2] Univ N Carolina, Dept Elect Comp & Engn, Charlotte, NC USA

来源：

2020 IEEE/ACM SYMPOSIUM ON EDGE COMPUTING (SEC 2020) | 2020年

关键词：

QoE; Q-Learning; Reinforcement Learning; Server Selection; Video Streaming; EXPERIENCE; QUALITY;

D O I：

10.1109/SEC50012.2020.00066

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Mobile devices make up the bulk of clients that stream video content over the internet. Improving one of the most popular services, i.e., mobile video streaming, has the potential to make the most market impact. Video streaming giants like YouTube, Netflix, Hulu, and Amazon video aim to provide the best quality service and expand market share. The problem of selecting the best server is critical for ensuring the qualified experience for video streaming on a mobile device. Traditional server selection strategies use proximity as a server selection rule. Improved strategies select servers by considering more factors that also impact the quality of experience (QoE). Currently, reinforcement learning is being used to maximize QoE when selecting servers. This paper seeks to further develop an RL agent that performs better on mobile devices. The result is an RL agent that quickly learns to select servers that offer the best QoE.

引用

页码：435 / 439

页数：5

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