The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
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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Cites
Dynamic adaptive streaming over HTTP --
Stockhammer, T. (2011) · doi:10.1145/1943552.1943572 · Cited by 1,236
An experimental evaluation of rate-adaptation algorithms in adaptive streaming over HTTP
Akhshabi, S., Begen, A. C., & Dovrolis, C. (2011) · doi:10.1145/1943552.1943574 · Cited by 616
Measuring the quality of experience of HTTP video streaming
Mok, R. K. P., Chan, E. W. W., & Chang, R. K. C. (2011) · doi:10.1109/inm.2011.5990550 · Cited by 478
Commute path bandwidth traces from 3G networks
Riiser, H., Vigmostad, P., Griwodz, C., & Halvorsen, P. (2013) · ACM Multimedia Systems Conference (MMSys) · doi:10.1145/2483977.2483991 · Cited by 367
Adaptation algorithm for adaptive streaming over HTTP
Miller, K., Quacchio, E., Gennari, G., & Adam Wolisz, A. (2012) · International Packet Video Workshop (PV) · doi:10.1109/pv.2012.6229732 · Cited by 238
Model for estimating QoE of video delivered using HTTP adaptive streaming
Vriendt, J. D., Vleeschauwer, D. D., & Robinson, D. C. (2013) · Integrated Network Management · Cited by 85
On the merits of SVC-based HTTP Adaptive Streaming
Famaey, J., Latré, S., Bouten, N., de Meerssche, W. V., Vleeschauwer, B. D., Leekwijck, W. V., & Turck, F. D. (2013) · Ghent University Academic Bibliography (Ghent University) · Cited by 67
Self-citationDesign of a Q-learning-based client quality selection algorithm for HTTP adaptive video streaming
Claeys, M., Latré, S., Famaey, J., Wu, T., Leekwijck, W. V., & Turck, F. D. (2013) · Ghent University Academic Bibliography (Ghent University) · Cited by 63
Self-citationIntelligent control for adaptive video streaming
Menkovski, V., & Liotta, A. (2013) · International Conference on Consumer Electronics · doi:10.1109/icce.2013.6486825 · Cited by 43
QoE optimization through in-network quality adaptation for HTTP adaptive streaming
(2012) · Ghent University Academic Bibliography (Ghent University) · doi:10.5555/2499406.2499460 · Cited by 36
Self-citation
Research works printed but not linked: 1
- sutton, 1998, Reinforcement Learning An Introduction (Adaptive Computation and Machine Learning)
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Sorted by citations
Items 101–111 of 111
Glisic, S., & Lorenzo, B. (2022) · Wiley eBooks · doi:10.1002/9781119790327.ch7
Song, W. (2020) · Springer eBooks · doi:10.1007/978-3-319-78262-1_60
Internet Multimedia Content Distribution
Li, Z.-N., Drew, M. S., & Liu, J. (2021) · Texts in computer science · doi:10.1007/978-3-030-62124-7_16
Al-Azzeh, J., Одарченко, Р. С., Abakumova, A., & Bondar, S. V. (2020) · doi:10.21203/rs.3.rs-104010/v1
An SDN-aided low-latency live video streaming over HTTP
Ozcelik, I. M., & Ersoy, C. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12389-y
Reducing QoE Loss and Data Wastage of Short-form Video Streaming with HTTP/3 Prioritization
Tran, C. M., Duc, T. N., Bach, N. G., Tan, P. X., & Kamioka, E. (2023) · International Conference on Informatics, Environment, Energy and Applications (IEEA) · doi:10.1145/3594692.3594741
den Hengst, F. (2023) · doi:10.5463/thesis.390
Saini, S. S., & Sharma, L. S. (2025) · Journal of The Institution of Engineers (India) Series B · doi:10.1007/s40031-025-01244-x
Adaptation of CDN at the Edge Using Cloud-Native Network Telemetry Across Media Scenarios
Iglesias, J., Mogollón, F., Tamayo, I., Fernández, Z., Danielsson, O., Pretel, I., & Lopez, A. (2025) · International Symposium on Multimedia · doi:10.1109/ism66958.2025.00065
Multi-Criteria Optimization of Content Delivery within the Future Media Internet
Bruneau‐Queyreix, J. (2017) · theses.fr (ABES) · doi:10.70675/66faf773ze4dfz467czafcaz119590a03ccb
Adaptive streaming using Peer-to-Peer and HTTP
Yousef, H. (2021) · theses.fr (ABES) · doi:10.70675/3987b411z115ez4e73z99c2zeda04f8defc8
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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