Incorporating prediction into adaptive streaming algorithms: a QoE perspective

Show simple item record

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 2018-07-03T13:38:36Z
dc.date.available 2018-07-03T13:38:36Z
dc.date.issued 2018-06
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. (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: 10.1145/3210445.3210457 en
dc.identifier.startpage 49 en
dc.identifier.endpage 54 en
dc.identifier.isbn 978-1-4503-5772-2
dc.identifier.uri http://hdl.handle.net/10468/6409
dc.identifier.doi 10.1145/3210445.3210457
dc.description.abstract Streaming 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.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Association for Computing Machinery, ACM en
dc.relation.ispartof NOSSDAV '18 Proceedings of the 28th ACM SIGMM Workshop on Network and Operating Systems Support for Digital Audio and Video (MMSysâ 18)
dc.relation.uri https://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/10.1145/3210445.3210457 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 video streaming en
dc.subject Information systems en
dc.subject Multimedia streaming en
dc.subject Networks en
dc.subject Public Internet en
dc.subject Wireless access networks en
dc.subject Network measurement en
dc.title Incorporating prediction into adaptive streaming algorithms: a QoE perspective 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 2018-07-03T12:02:01Z
dc.description.version Accepted Version en
dc.internal.rssid 443941711
dc.contributor.funder Science Foundation Ireland en
dc.description.status Peer reviewed en
dc.identifier.journaltitle NOSSDAV '18 Proceedings of the 28th ACM SIGMM Workshop on Network and Operating Systems Support for Digital Audio and Video en
dc.internal.copyrightchecked No !!CORA!! en
dc.internal.licenseacceptance Yes en
dc.internal.conferencelocation Amsterdam, The Netherlands 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


Files in this item

This item appears in the following Collection(s)

Show simple item record

This website uses cookies. By using this website, you consent to the use of cookies in accordance with the UCC Privacy and Cookies Statement. For more information about cookies and how you can disable them, visit our Privacy and Cookies statement