Research on Q-learning based rate control approach for HTTP adaptive streaming

被引：0

作者：

Xiong, Li-Rong ^{[1
]}

Lei, Jing-Zhi ^{[1
]}

Jin, Xin ^{[1
]}

机构：

[1] College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou,310023, China

来源：

Tongxin Xuebao/Journal on Communications | 2017年 / 38卷 / 09期

关键词：

Video streaming - Learning algorithms - Quality of service - Reinforcement learning;

D O I：

10.11959/j.issn.1000-436x.2017178

中图分类号：

学科分类号：

摘要：

HTTP adaptive streaming (HAS) has become the standard for adaptive video streaming service. In changing network environments, current hardcoded-based rate adaptation algorithm was less flexible, and it is insufficient to consider the quality of experience (QoE). To optimize the QoE of users, a rate control approach based on Q-learning strategy was proposed. the client environments of HTTP adaptive video streaming was modeled and the state transition rule was defined. Three parameters related to QoE were quantified and a novel reward function was constructed. The experiments were employed by the Q-learning rate control approach in two typical HAS algorithms. The experiments show the rate control approach can enhance the stability of rate switching in HAS clients. © 2017, Editorial Board of Journal on Communications. All right reserved.

引用

页码：18 / 24