Title
Design and optimisation of a (FA)Q-learning-based HTTP adaptive streaming client Design and optimisation of a (FA)Q-learning-based HTTP adaptive streaming client
Author
Faculty/Department
Faculty of Sciences. Mathematics and Computer Science
Publication type
article
Publication
Sheffield ,
Subject
Computer. Automation
Source (journal)
Connection science : a journal of neural computing, artificial intelligence and cognitive research. - Sheffield
Volume/pages
26(2014) :1 , 20 p.
ISSN
0954-0091
1360-0494
0954-0091
ISI
000333949100004
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
In recent years, HTTP (Hypertext Transfer Protocol) adaptive streaming (HAS) has become the de facto standard for adaptive video streaming services. A HAS video consists of multiple segments, encoded at multiple quality levels. State-of-the-art HAS clients employ deterministic heuristics to dynamically adapt the requested quality level based on the perceived network conditions. Current HAS client heuristics are, however, hardwired to fit specific network configurations, making them less flexible to fit a vast range of settings. In this article, a (frequency adjusted) Q-learning HAS client is proposed. In contrast to existing heuristics, the proposed HAS client dynamically learns the optimal behaviour corresponding to the current network environment in order to optimise the quality of experience. Furthermore, the client has been optimised both in terms of global performance and convergence speed. Thorough evaluations show that the proposed client can outperform deterministic algorithms by 11-18% in terms of mean opinion score in a wide range of network configurations.
E-info
https://repository.uantwerpen.be/docman/iruaauth/3e38d0/9007640.pdf
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