SMASH: a Supervised Machine Learning Approach to Adaptive Video Streaming over HTTP.

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dc.contributor.authorSani, Yusuf
dc.contributor.authorRaca, Darijo
dc.contributor.authorQuinlan, Jason J.
dc.contributor.authorSreenan, Cormac J.
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2020-04-15T10:31:39Z
dc.date.available2020-04-15T10:31:39Z
dc.date.issued2020-05
dc.date.updated2020-04-15T10:22:34Z
dc.description.abstractThe growth of online video-on-demand consumption continues unabated. Existing heuristic-based adaptive bitrate (ABR) selection algorithms are typically designed to optimise video quality within a very narrow context. This may lead to video streaming providers implementing different ABR algorithms/players, based on a network connection, device capabilities, video content, etc., in order to serve the multitude of their usersâ streaming requirements. In this paper, we present SMASH: a Supervised Machine learning approach to Adaptive Streaming over HTTP, which takes a tentative step towards the goal of a one-size-fits-all approach to ABR. We utilise the streaming output from the adaptation logic of nine ABR algorithms across a variety of streaming scenarios (generating nearly one million records) and design a machine learning model, using systematically selected features, to predict the optimal choice of the bitrate of the next video segment to download. Our evaluation results show that not only does SMASH guarantee a high QoE but its performance is consistent across a variety of streaming contexts.en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationSani, Y., Raca, D., Quinlan, J. J. and Sreenan, C. J. (2020) 'SMASH: A Supervised Machine Learning Approach to Adaptive Video Streaming over HTTP', 2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX), 26-28 May 2020, 1-6, doi: 10.1109/QoMEX48832.2020.9123139en
dc.identifier.doi10.1109/QoMEX48832.2020.9123139en
dc.identifier.endpage6en
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/9829
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Investigator Programme/13/IA/1892/IE/An Internet Infrastructure for Video Streaming Optimisation (iVID)/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/13/RC/2077/IE/CONNECT: The Centre for Future Networks & Communications/en
dc.relation.urihttps://ieeexplore.ieee.org/document/9123139
dc.rights© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en
dc.subjectSMASHen
dc.subjectHASen
dc.subjectHTTP Adaptive Streaming,en
dc.subjectDASHen
dc.subjectMachine Learningen
dc.titleSMASH: a Supervised Machine Learning Approach to Adaptive Video Streaming over HTTP.en
dc.typeConference itemen
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