|
Title
|
|
|
|
Design and evaluation of a self-learning HTTP adaptive video streaming client
| |
|
Author
|
|
|
|
| |
|
Abstract
|
|
|
|
| HTTP Adaptive Streaming (HAS) is becoming the de facto standard for Over-The-Top (OTT)-based video streaming services such as YouTube and Netflix. By splitting a video into multiple segments of a couple of seconds and encoding each of these at multiple quality levels, HAS allows a video client to dynamically adapt the requested quality during the playout to react to network changes. However, state-of-the-art quality selection heuristics are deterministic and tailored to specific network configurations. Therefore, they are unable to cope with a vast range of highly dynamic network settings. In this letter, a novel Reinforcement Learning (RL)-based HAS client is presented and evaluated. The self-learning HAS client dynamically adapts its behaviour by interacting with the environment to optimize the Quality of Experience (QoE), the quality as perceived by the end-user. The proposed client has been thoroughly evaluated using a network-based simulator and is shown to outperform traditional HAS clients by up to 13% in a mobile network environment. |
| |
|
Language
|
|
|
|
English
| |
|
Source (journal)
|
|
|
|
IEEE communications letters / Institute of Electrical and Electronics Engineers, Communications Society. - New York, N.Y.
| |
|
Publication
|
|
|
|
New York, N.Y.
:
2014
| |
|
ISSN
|
|
|
|
1089-7798
| |
|
DOI
|
|
|
|
10.1109/LCOMM.2014.020414.132649
| |
|
Volume/pages
|
|
|
|
18
:4
(2014)
, p. 716-719
| |
|
ISI
|
|
|
|
000335400100048
| |
|
Full text (Publisher's DOI)
|
|
|
|
| |
|