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Empowerment of atypical viewers via low-effort personalized modeling of video streaming quality

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Identificadores
URI: https://hdl.handle.net/20.500.12761/1759
ISSN: 2834-5509
DOI: 10.1145/3629139
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Autor(es)
Peroni, Leonardo; Gorinsky, Sergey; Tashtarian, Farzad; Timmerer, Christian
Fecha
2023-12
Resumen
Quality 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.
Compartir
Ficheros
Main article (4.779Mb)
Identificadores
URI: https://hdl.handle.net/20.500.12761/1759
ISSN: 2834-5509
DOI: 10.1145/3629139
Metadatos
Mostrar el registro completo del ítem

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