Realistic video sequences for subjective QoE analysis

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dc.contributor.authorHodzic, Kerim
dc.contributor.authorCosovic, Mirsad
dc.contributor.authorMrdovic, Sasa
dc.contributor.authorQuinlan, Jason J.
dc.contributor.authorRaca, Darijo
dc.contributor.funderMinistry of Education, Science and Youth of Sarajevo Cantonen
dc.date.accessioned2022-04-20T13:09:46Z
dc.date.available2022-04-20T13:09:46Z
dc.date.issued2022
dc.date.updated2022-04-20T10:01:47Z
dc.description.abstractMultimedia streaming over the Internet (live and on demand) is the cornerstone of modern Internet carrying more than 60% of all traffic. With such high demand, delivering outstanding user experience is a crucial and challenging task. To evaluate user QoE many researchers deploy subjective quality assessments where participants watch and rate videos artificially infused with various temporal and spatial impairments. To aid current efforts in bridging the gap between the mapping of objective video QoE metrics to user experience, we developed DashReStreamer, an open-source framework for re-creating adaptively streamed video in real networks. DashReStreamer utilises a log created by a HAS algorithm run in an uncontrolled environment (i.e., wired or wireless networks), encoding visual changes and stall events in one video file. These videos are applicable for subjective QoE evaluation mimicking realistic network conditions. To supplement DashReStreamer, we re-create 234 realistic video clips, based on video logs collected from real mobile and wireless networks. In addition our dataset contains both video logs with all decisions made by the HASalgorithm and network bandwidth profile illustrating throughput distribution. We believe this dataset and framework will permit other researchers in their pursuit for the final frontier in understanding the impact of video QoE dynamics.en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationHodzic, K., Cosovic, M., Mrdovic, S., Quinlan, J. J. and Raca, D. (2022) ‘Realistic video sequences for subjective QoE analysis’, MMSys '22: Proceedings of the 13th ACM Multimedia Systems Conference, Athlone, Ireland, 14-17 June, pp. 246-251. doi: 10.1145/3524273.3532894en
dc.identifier.doi10.1145/3524273.3532894
dc.identifier.endpage251
dc.identifier.isbn978-1-4503-9283-9
dc.identifier.startpage246
dc.identifier.urihttps://hdl.handle.net/10468/13099
dc.language.isoenen
dc.publisherAssociation for Computing Machinery (ACM)en
dc.relation.urihttps://mmsys2022.ie/
dc.rights© 2022, Association for Computing Machinery. This is the author's version of the work. The definitive Version of Record is available at: https://dx.doi.org/10.1145/3524273.3532894.en
dc.subjectQoEen
dc.subjectDataseten
dc.subjectMobilityen
dc.subjectThroughputen
dc.subjectContext informationen
dc.subjectAdaptive video streamingen
dc.subject3Gen
dc.subject4Gen
dc.subject5Gen
dc.subjectWiFien
dc.titleRealistic video sequences for subjective QoE analysisen
dc.typeConference itemen
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