Publication
Title
Design and optimisation of a (FA)Q-learning-based HTTP adaptive streaming client
Author
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.
Language
English
Source (journal)
Connection science : a journal of neural computing, artificial intelligence and cognitive research. - Sheffield
Publication
Sheffield : 2014
ISSN
0954-0091 [print]
1360-0494 [online]
Volume/pages
26:1(2014), 20 p.
ISI
000333949100004
Full text (Publisher's DOI)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
[E?say:metaLocaldata.cgzprojectinf]
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 06.06.2014
Last edited 21.09.2017
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