QoE-traffic Optimization Through Collaborative Edge Caching in Adaptive Mobile Video Streaming

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorMehrabidavoodabadi, Abbasen_US
dc.contributor.authorSiekkinen, Mattien_US
dc.contributor.authorYlä-Jääski, Anttien_US
dc.contributor.departmentDepartment of Computer Scienceen
dc.contributor.groupauthorProfessorship Ylä-Jääski A.en
dc.contributor.groupauthorHelsinki Institute for Information Technology (HIIT)en
dc.date.accessioned2018-12-21T10:28:06Z
dc.date.available2018-12-21T10:28:06Z
dc.date.issued2018-09en_US
dc.description.abstractMulti-access edge computing has been proposed as a promising approach to localize the access of mobile clients to the network edges, therefore, reducing significantly the traffic congestion on the backhaul network. Due to time-varying wireless channel condition, the video caching at the mobile edges for dynamic adaptive video streaming over HTTP (DASH) needs to be efficiently handled to alleviate the high bandwidth demand on the backhaul network and improve the quality of experience (QoE) of end users. We investigate the impact of collaborative mobile edge caching on joint QoE and backhaul data traffic by proposing the joint QoE-traffic optimization with collaborative edge caching which introduces the BFTR (backhaul/fronthaul traffic ratio) parameter adjustable by the mobile network operator. We then design a self-tuned bitrate selection algorithm with low complexity to solve the optimization problem and further propose an efficient cache replacement strategy called retention-based collaborative caching. Through simulation-based evaluations, we show a noticeable gain in the percentage of cache miss and specify some threshold for BFTR parameter after which the significant reduction in the data traffic with further improvement in average video bitrate is obtained using collaborative caching. Our findings help mobile edge system developers design an efficient collaborative caching mechanism for 5G networks.en
dc.description.versionPeer revieweden
dc.format.extent15
dc.format.extent52261-52276
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationMehrabidavoodabadi, A, Siekkinen, M & Ylä-Jääski, A 2018, ' QoE-traffic Optimization Through Collaborative Edge Caching in Adaptive Mobile Video Streaming ', IEEE Access, vol. 6, pp. 52261-52276 . https://doi.org/10.1109/ACCESS.2018.2870855en
dc.identifier.doi10.1109/ACCESS.2018.2870855en_US
dc.identifier.issn2169-3536
dc.identifier.otherPURE UUID: 055a8e20-5c5e-44c4-bc22-dff6818125deen_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/055a8e20-5c5e-44c4-bc22-dff6818125deen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/30325427/08467314.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/35607
dc.identifier.urnURN:NBN:fi:aalto-201812216615
dc.language.isoenen
dc.relation.ispartofseriesIEEE Accessen
dc.relation.ispartofseriesVolume 6en
dc.rightsopenAccessen
dc.subject.keywordCollaborative caching, Dynamic adaptive video streaming over HTTP (DASH), Fairness, Integer non-linear programming, Multi-access edge computing (MEC), NP-hardness, Quality of experienceen_US
dc.subject.keywordDynamic adaptive video streaming over HTTP (DASH)en_US
dc.subject.keywordFairnesen_US
dc.subject.keywordInteger non-linear programmingen_US
dc.subject.keywordMulti-access edge computing (MEC)en_US
dc.subject.keywordNP-hardnesen_US
dc.subject.keywordQuality of experienceen_US
dc.titleQoE-traffic Optimization Through Collaborative Edge Caching in Adaptive Mobile Video Streamingen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion
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