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Design and evaluation of a self-learning HTTP adaptive video streaming client

Maxim Claeys (UGent) , Steven Latré (UGent) , Jeroen Famaey (UGent) and Filip De Turck (UGent)
(2014) IEEE COMMUNICATIONS LETTERS. 18(4). p.716-719
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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.
Keywords
learning systems, Streaming media, intelligent agent, quality of service, IBCN

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Citation

Please use this url to cite or link to this publication:

MLA
Claeys, Maxim, et al. “Design and Evaluation of a Self-Learning HTTP Adaptive Video Streaming Client.” IEEE COMMUNICATIONS LETTERS, vol. 18, no. 4, 2014, pp. 716–19, doi:10.1109/LCOMM.2014.020414.132649.
APA
Claeys, M., Latré, S., Famaey, J., & De Turck, F. (2014). Design and evaluation of a self-learning HTTP adaptive video streaming client. IEEE COMMUNICATIONS LETTERS, 18(4), 716–719. https://doi.org/10.1109/LCOMM.2014.020414.132649
Chicago author-date
Claeys, Maxim, Steven Latré, Jeroen Famaey, and Filip De Turck. 2014. “Design and Evaluation of a Self-Learning HTTP Adaptive Video Streaming Client.” IEEE COMMUNICATIONS LETTERS 18 (4): 716–19. https://doi.org/10.1109/LCOMM.2014.020414.132649.
Chicago author-date (all authors)
Claeys, Maxim, Steven Latré, Jeroen Famaey, and Filip De Turck. 2014. “Design and Evaluation of a Self-Learning HTTP Adaptive Video Streaming Client.” IEEE COMMUNICATIONS LETTERS 18 (4): 716–719. doi:10.1109/LCOMM.2014.020414.132649.
Vancouver
1.
Claeys M, Latré S, Famaey J, De Turck F. Design and evaluation of a self-learning HTTP adaptive video streaming client. IEEE COMMUNICATIONS LETTERS. 2014;18(4):716–9.
IEEE
[1]
M. Claeys, S. Latré, J. Famaey, and F. De Turck, “Design and evaluation of a self-learning HTTP adaptive video streaming client,” IEEE COMMUNICATIONS LETTERS, vol. 18, no. 4, pp. 716–719, 2014.
@article{5733061,
  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.}},
  author       = {{Claeys, Maxim and Latré, Steven and Famaey, Jeroen and De Turck, Filip}},
  issn         = {{1089-7798}},
  journal      = {{IEEE COMMUNICATIONS LETTERS}},
  keywords     = {{learning systems,Streaming media,intelligent agent,quality of service,IBCN}},
  language     = {{eng}},
  number       = {{4}},
  pages        = {{716--719}},
  title        = {{Design and evaluation of a self-learning HTTP adaptive video streaming client}},
  url          = {{http://doi.org/10.1109/LCOMM.2014.020414.132649}},
  volume       = {{18}},
  year         = {{2014}},
}
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