AVIRA: Enhanced multipath for content-aware adaptive Virtual Reality

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dc.contributor.authorSilva, Fábio
dc.contributor.authorTogou, Mohammed Amine
dc.contributor.authorMuntean, Gabriel-Miro
dc.contributor.funderHorizon 2020en
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderEuropean Regional Development Funden
dc.date.accessioned2022-11-01T14:44:46Z
dc.date.available2022-11-01T14:44:46Z
dc.date.issued2020-07-27
dc.date.updated2022-11-01T14:21:01Z
dc.description.abstractThis paper presents Adaptive VR (AVIRA), a scheme that implements a Virtual Reality (VR) content-aware prioritisation transport to extend Multipath TCP (MPTCP) functionalities and improve its performance. To do so, AVIRA monitors the subflows operation and forecasts subflows' performance by applying an Machine Learning (ML) approach to evaluate a set of features - such as latency and throughput - for every subflow available. This ML approach forecasts the performance of these features through linear regression and applies a linear classifier by using a weighted sum on the forecast results. When the traffic of a specific VR component is detected, AVIRA performs its prioritisation scheme by redirecting packets to the subflow with the best set of forecasted features. AVIRA outperforms the algorithms used for comparison and shows that the use of an ML approach in a 'low-level' application is viable, especially in situations where the network features under scrutiny are subject to higher variations. In these scenarios, the AVIRA scheme can be outstandingly efficient.en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationSilva, F., Togou, M. A. and Muntean, G.-M. (2020) 'AVIRA: Enhanced multipath for content-aware adaptive Virtual Reality', 2020 International Wireless Communications and Mobile Computing (IWCMC), Limassol, Cyprus, 15-19 June, pp. 917-922. doi: 10.1109/IWCMC48107.2020.9148293en
dc.identifier.doi10.1109/IWCMC48107.2020.9148293en
dc.identifier.eissn2376-6506
dc.identifier.endpage922en
dc.identifier.isbn978-1-7281-3129-0
dc.identifier.isbn978-1-7281-3128-3
dc.identifier.isbn978-1-7281-3130-6
dc.identifier.issn2376-6492
dc.identifier.startpage917en
dc.identifier.urihttps://hdl.handle.net/10468/13795
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.projectinfo:eu-repo/grantAgreement/EC/H2020::IA/688503/EU/Networked Labs for Training in Sciences and Technologies for Information and Communication/NEWTONen
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/13/RC/2094/IE/Lero - the Irish Software Research Centre/en
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.subjectMachine learningen
dc.subjectMultipath TCPen
dc.subjectNetwork transport improvementen
dc.subjectNeural networken
dc.subjectRegressionen
dc.subjectVirtual realityen
dc.titleAVIRA: Enhanced multipath for content-aware adaptive Virtual Realityen
dc.typeConference itemen
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