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

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dc.contributor.author Raca, Darijo
dc.contributor.author Zahran, Ahmed H.
dc.contributor.author Sreenan, Cormac J.
dc.contributor.author Sinha, Rakesh K.
dc.contributor.author Halepovic, Emir
dc.contributor.author Jana, Rittwik
dc.contributor.author Gopalakrishnan, Vijay
dc.contributor.author Bathula, Balagangadhar
dc.contributor.author Varvello, Matteo
dc.date.accessioned 2019-07-15T09:13:34Z
dc.date.available 2019-07-15T09:13:34Z
dc.date.issued 2019-06
dc.identifier.citation Raca, 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: 10.1145/3304109.3306233 en
dc.identifier.startpage 201 en
dc.identifier.endpage 212 en
dc.identifier.isbn 978-1-4503-6297-9
dc.identifier.uri http://hdl.handle.net/10468/8162
dc.identifier.doi 10.1145/3304109.3306233 en
dc.description.abstract Today'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.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Association for Computing Machinery (ACM) en
dc.relation.uri https://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/10.1145/3304109.3306233 en
dc.subject HAS en
dc.subject 4G en
dc.subject LTE en
dc.subject Mobility en
dc.subject Throughput prediction en
dc.subject DASH en
dc.subject Adaptive en
dc.subject Video streaming en
dc.title Empowering video players in cellular: throughput prediction from radio network measurements en
dc.type Conference item en
dc.internal.authorcontactother Cormac Sreenan, Computer Science, University College Cork, Cork, Ireland. +353-21-490-3000 Email: c.sreenan@cs.ucc.ie en
dc.internal.availability Full text available en
dc.date.updated 2019-07-15T08:58:01Z
dc.description.version Accepted Version en
dc.internal.rssid 492900560
dc.contributor.funder Science Foundation Ireland en
dc.description.status Peer reviewed en
dc.internal.copyrightchecked Yes
dc.internal.licenseacceptance Yes en
dc.internal.conferencelocation Amherst, Massachusetts en
dc.internal.IRISemailaddress c.sreenan@cs.ucc.ie en
dc.relation.project info:eu-repo/grantAgreement/SFI/SFI Investigator Programme/13/IA/1892/IE/An Internet Infrastructure for Video Streaming Optimisation (iVID)/ en

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