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Design and Evaluation of a Self-Learning HTTP Adaptive Video Streaming Client
Open accessAbstract
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.
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“The authors claim that the Q-learning client achieves in average 9.12% higher estimated MOS (program-generated), with 16.65% lower standard deviation, than the traditional Microsoft ISS Smooth Streaming (MSS) client.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [102]
In the citing paper
“Data-driven approaches, in particular ML, have been employed mainly in two different ways: (1) to predict changes in network QoS, namely throughput, and trigger adaptation mechanism to reduce rebuffering time [432], and (2) to select appropriate adaptation action [102].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [102]
In the citing paper
“While some find it sufficient to correlate the QoE to a single network QoS parameter [145, 240, 383, 408], e.g.delay or throughput, others argue that multiple QoS parameters impact the QoE and need to be considered in tandem as features in a QoE/QoS correlation model [89,102,114,235,287,328,432].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [102]
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Adaptive Driving-Speed Control at Signalised Intersection Using Reinforcement Learning
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Layer approach for HTTP-based low-delay adaptive streaming in mobile networks
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Sobhani, A., Yassine, A., & Shirmohammadi, S. (2017) · IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) · doi:10.1109/pimrc.2017.8292778
Research on quality metrics of wireless adaptive video streaming
Li, X. (2018) · International Conference on Graphics and Image Processing (ICGIP) · doi:10.1117/12.2302493
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Shahab, M. B., & Shin, S. Y. (2018) · International Conference on Computer and Communication Engineering · doi:10.1109/iccce.2018.8539248
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A QoE Fairness Approach for Adaptive Video Streaming
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Metadata
- Authors
- Claeys, M., Latre, S., Famaey, J., & De Turck, F.
- Affiliations
- Ghent University (BE)
- Venue
- IEEE Communications Letters
- Published
- 2014-04
- Volume
- 18
- Issue
- 4
- Pages
- 716-719
- ISSN
- 1089-7798 (ISSN-L)
- OpenAlex
- W1998431331
- Cited by
- 111
- References
- 10
- Citations by year
- 2025: 3 · 2024: 3 · 2023: 6 · 2022: 9 · 2021: 8 · 2020: 13 · 2019: 15 · 2018: 22 · 2017: 10 · 2016: 11 · 2015: 9 · 2014: 2
- FWCI
- 9.54
- Type
- journal article
- Language
- en
- Open access
- green
- biblio.ugent.be
- Locations
- IEEE Communications Letters (publishedVersion)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion, other-oa)
- Funders
- European Commission (318488)
- Vlaamse regering
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Physical Sciences › Computer Science › Computer Networks and Communications › Caching and Content Delivery
- Keywords
- reinforcement learning, quality of experience, cellular networks, video segmentation
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