Empowering video players in cellular: throughput prediction from radio network measurements

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dc.contributor.authorRaca, Darijo
dc.contributor.authorZahran, Ahmed H.
dc.contributor.authorSreenan, Cormac J.
dc.contributor.authorSinha, Rakesh K.
dc.contributor.authorHalepovic, Emir
dc.contributor.authorJana, Rittwik
dc.contributor.authorGopalakrishnan, Vijay
dc.contributor.authorBathula, Balagangadhar
dc.contributor.authorVarvello, Matteo
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2019-07-15T09:13:34Z
dc.date.available2019-07-15T09:13:34Z
dc.date.issued2019-06
dc.date.updated2019-07-15T08:58:01Z
dc.description.abstractToday's HTTP adaptive streaming applications are designed to provide high levels of Quality of Experience (QoE) across a wide range of network conditions. The adaptation logic in these applications typically needs an estimate of the future network bandwidth for quality decisions. This estimation, however, is challenging in cellular networks because of the inherent variability of bandwidth and latency due to factors like signal fading, variable load, and user mobility. In this paper, we exploit machine learning (ML) techniques on a range of radio channel metrics and throughput measurements from a commercial cellular network to improve the estimation accuracy and hence, streaming quality. We propose a novel summarization approach for input raw data samples. This approach reduces the 90th percentile of absolute prediction error from 54% to 13%. We evaluate our prediction engine in a trace-driven controlled lab environment using a popular Android video player (ExoPlayer) running on a stock mobile device and also validate it in the commercial cellular network. Our results show that the three tested adaptation algorithms register improvement across all QoE metrics when using prediction, with stall reduction up to 85% and bitrate switching reduction up to 40%, while maintaining or improving video quality. Finally, prediction improves the video QoE score by up to 33%.en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationRaca, D., Zahran, A. H., Sreenan, C. J., Sinha, R. K., Halepovic, E., Jana, R., Gopalakrishnan, V., Bathula, B. and Varvello, M. (2019) ‘Empowering video players in cellular: throughput prediction from radio network measurements’, Proceedings of the 10th ACM Multimedia Systems Conference, Amherst, Massachusetts, 18-21 June, pp. 201-212. doi: 10.1145/3304109.3306233en
dc.identifier.doi10.1145/3304109.3306233en
dc.identifier.endpage212en
dc.identifier.isbn978-1-4503-6297-9
dc.identifier.startpage201en
dc.identifier.urihttps://hdl.handle.net/10468/8162
dc.language.isoenen
dc.publisherAssociation for Computing Machinery (ACM)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.urihttps://dl.acm.org/citation.cfm?id=3306233
dc.rights© 2019, Association for Computing Machinery. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 10th ACM Multimedia Systems Conference: https://doi.org/10.1145/3304109.3306233en
dc.subjectHASen
dc.subject4Gen
dc.subjectLTEen
dc.subjectMobilityen
dc.subjectThroughput predictionen
dc.subjectDASHen
dc.subjectAdaptiveen
dc.subjectVideo streamingen
dc.titleEmpowering video players in cellular: throughput prediction from radio network measurementsen
dc.typeConference itemen
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