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dc.contributor.authorPeroni, Leonardo 
dc.contributor.authorGorinsky, Sergey 
dc.contributor.authorTashtarian, Farzad
dc.contributor.authorTimmerer, Christian
dc.date.accessioned2023-10-23T10:54:02Z
dc.date.available2023-10-23T10:54:02Z
dc.date.issued2023-12
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dc.identifier.issn2834-5509es
dc.identifier.urihttps://hdl.handle.net/20.500.12761/1759
dc.descriptionThis is a CoNEXT 2023 conference item published in the journal.es
dc.description.abstractQuality of Experience (QoE) and QoE models are of an increasing importance to networked systems. The traditional QoE modeling for video streaming applications builds a one-size-fits-all QoE model that underserves atypical viewers who perceive QoE differently. To address the problem of atypical viewers, this paper proposes iQoE (individualized QoE), a method that employs explicit, expressible, and actionable feedback from a viewer to construct a personalized QoE model for this viewer. The iterative iQoE design exercises active learning and combines a novel sampler with a modeler. The chief emphasis of our paper is on making iQoE sample-efficient and accurate. By leveraging the Microworkers crowdsourcing platform, we conduct studies with 120 subjects who provide 14,400 individual scores. According to the subjective studies, a session of about 22 minutes empowers a viewer to construct a personalized QoE model that, compared to the best of the 10 baseline models, delivers the average accuracy improvement of at least 42% for all viewers and at least 85% for the atypical viewers. The large-scale simulations based on a new technique of synthetic profiling expand the evaluation scope by exploring iQoE design choices, parameter sensitivity, and generalizability.es
dc.description.sponsorshipSpanish Ministry of Science and Innovationes
dc.description.sponsorshipAustrian Federal Ministry for Digital and Economic Affairs, National Foundation for Research, Technology and Development, and Christian Doppler Research Associationes
dc.language.isoenges
dc.publisherACMes
dc.titleEmpowerment of atypical viewers via low-effort personalized modeling of video streaming qualityes
dc.typejournal articlees
dc.journal.titleProceedings of the ACM on Networkinges
dc.type.hasVersionAMes
dc.rights.accessRightsopen accesses
dc.volume.number1es
dc.issue.numberCoNEXT3es
dc.identifier.doi10.1145/3629139es
dc.page.final27es
dc.page.initial1es
dc.relation.projectNameSocialProbing (Scalable and Cost Competitive Data Collection and Analysis Techniques for Social Probing) and GreenEdge (Energy-efficient Monitoring in the era of Edge Intelligence)es
dc.relation.projectNameChristian Doppler Laboratory ATHENAes
dc.subject.keywordvideo streaminges
dc.subject.keywordpersonalizationes
dc.subject.keywordquality of experiencees
dc.subject.keywordmodelinges
dc.subject.keywordsample efficiencyes
dc.subject.keywordaccuracyes
dc.subject.keywordsubjective studyes
dc.subject.keywordperception datasetes
dc.subject.keywordpersonalized QoE modeles
dc.description.refereedTRUEes
dc.description.statuspubes


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