Towards Optimal Multiview Transcoding for Edge-Assisted Wireless Volumetric Streaming

Current wireless volumetric video streaming faces multiple challenges, including high bandwidth consumption, large decoding overhead on the client device, and low view reconstruction quality perceived by the user. In this paper, we explore an edge-assisted streaming system, where 3D contents of volumetric video are first transcoded by the edge server based on predicted user positions and then transmitted to the client to substantially reduce the bandwidth consumption and decoding overhead. In such a system, however, inaccuracy in position prediction could lead to displacement between the transcoded reference view and the actual view required by the client. To mitigate this problem, we task the edge server to transcode multiple views and formulate the problem of multiview generation as an optimal quantization problem. Rather than using distance-based distortion metrics, our formulation considers the actual distortion perceived by the user for solving the general problem of positioning reference views under general position error distribution caused by position predictors. Our evaluations show significant improvements over state-of-the-art techniques by reducing quality degradation from 17% to 28% in terms of the 75-th and 95-th percentiles of distortion perceived by users.