Incorporating prediction into adaptive streaming algorithms: a QoE perspective

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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.accessioned2018-07-03T13:38:36Z
dc.date.available2018-07-03T13:38:36Z
dc.date.issued2018-06
dc.date.updated2018-07-03T12:02:01Z
dc.description.abstractStreaming over the wireless channel is challenging due to rapid fluctuations in available throughput. Encouraged by recent advances in cellular throughput prediction based on radio link metrics, we examine the impact on Quality of Experience (QoE) when using prediction within existing algorithms based on the DASH standard. By design, DASH algorithms estimate available throughput at the application level from chunk rates and then apply some averaging function. We investigate alternatives for modifying these algorithms, by providing the algorithms direct predictions in place of estimates or feeding predictions in place of measurement samples. In addition, we explore different prediction horizons going from one to three chunk durations. Furthermore, we induce different levels of error to ideal prediction values to analyse deterioration in user QoE as a function of average error. We find that by applying accurate prediction to three algorithms, user QoE can improve up to 55% depending on the algorithm in use. Furthermore having longer horizon positively affects QoE metrics. Accurate predictions have the most significant impact on stall performance by completely eliminating them. Prediction also improves switching behaviour significantly and longer prediction horizons enable a client to promptly reduce quality and avoid stalls when the throughput drops for a relatively long time that can deplete the buffer. For all algorithms, a 3-chunk horizon strikes the best balance between different QoE metrics and, as a result, achieving highest user QoE. While error-induced predictions significantly lower user QoE in certain situations, on average, they provide 15% improvement over DASH algorithms without any prediction.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. (2018) 'Incorporating Prediction into Adaptive Streaming Algorithms: A QoE Perspective', NOSSDAV '18 Proceedings of the 28th ACM SIGMM Workshop on Network and Operating Systems Support for Digital Audio and Video, Amsterdam, Netherlands, 12-15 June, 3210457: ACM, 49-54. doi: 10.1145/3210445.3210457en
dc.identifier.doi10.1145/3210445.3210457
dc.identifier.endpage54en
dc.identifier.isbn978-1-4503-5772-2
dc.identifier.journaltitleNOSSDAV '18 Proceedings of the 28th ACM SIGMM Workshop on Network and Operating Systems Support for Digital Audio and Videoen
dc.identifier.startpage49en
dc.identifier.urihttps://hdl.handle.net/10468/6409
dc.language.isoenen
dc.publisherAssociation for Computing Machinery, ACMen
dc.relation.ispartofNOSSDAV '18 Proceedings of the 28th ACM SIGMM Workshop on Network and Operating Systems Support for Digital Audio and Video (MMSysâ 18)
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?doid=3210445.3210457
dc.rights© 2018 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 NOSSDAV '18 Proceedings of the 28th ACM SIGMM Workshop on Network and Operating Systems Support for Digital Audio and Video, http://dx.doi.org/10.1145/3210445.3210457en
dc.subjectHASen
dc.subject4Gen
dc.subjectLTEen
dc.subjectMobilityen
dc.subjectThroughput predictionen
dc.subjectDASHen
dc.subjectAdaptive video streamingen
dc.subjectInformation systemsen
dc.subjectMultimedia streamingen
dc.subjectNetworksen
dc.subjectPublic Interneten
dc.subjectWireless access networksen
dc.subjectNetwork measurementen
dc.titleIncorporating prediction into adaptive streaming algorithms: a QoE perspectiveen
dc.typeConference itemen
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