Enabling Personalized Video Quality Optimization with VidHoc
Xu Zhang, Paul Schmitt, Marshini Chetty, Nick Feamster, Junchen Jiang

TL;DR
VidHoc is an innovative online system that personalizes and optimizes video quality for individual users in real-time, significantly reducing bandwidth usage while maintaining or improving user experience.
Contribution
It introduces the first automated online approach for per-user QoE modeling and optimization in video streaming, enabling quick adaptation for new users.
Findings
VidHoc reduces bandwidth by 17.3% while maintaining QoE.
It improves QoE by 13.9% at the same bandwidth.
The system effectively personalizes QoE models within a few video sessions.
Abstract
The emerging video applications greatly increase the demand in network bandwidth that is not easy to scale. To provide higher quality of experience (QoE) under limited bandwidth, a recent trend is to leverage the heterogeneity of quality preferences across individual users. Although these efforts have suggested the great potential benefits, service providers still have not deployed them to realize the promised QoE improvement. The missing piece is an automation of online per-user QoE modeling and optimization scheme for new users. Previous efforts either optimize QoE by known per-user QoE models or learn a user's QoE model by offline approaches, such as analysis of video viewing history and in-lab user study. Relying on such offline modeling is problematic, because QoE optimization will start late for collecting enough data to train an unbiased QoE model. In this paper, we propose…
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Taxonomy
TopicsImage and Video Quality Assessment · Video Coding and Compression Technologies · Advanced Computing and Algorithms
