Quality-Constrained Encoding Optimization for Omnidirectional Video Streaming

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Abstract
Omnidirectional video streaming is usually implemented based on the representations of tiles, where the tiles are obtained by splitting the video frame into several rectangular areas and each tile is converted into multiple representations with different resolutions and encoded at different bitrates. One key issue in omnidirectional video streaming is how to choose the optimal representations for each tile at the server to save the overall transmission bitrate to all users while offering them satisfactory quality. This is different from the adaptive bitrate-based method that optimizes the downloading procedure of individual users, where the given video representations are stored on the server. In this work, we focus on optimization for the encoding of omnidirectional video streaming by using the optimal combination of tile representations. To achieve our goal, we formulate the selection of the representations into an optimization problem in which the transmission bitrate of all the representations is minimized with a quality constraint. By using this constraint, we can improve the average quality of omnidirectional videos for users. More specifically, we first construct the tile-level rate-distortion (R-D) model and determine the available tile bandwidth based on the previous viewers’ statistics. Then, we formulate the representation selection problem based on the obtained R-D model and tile bandwidth. Finally, we solve this problem to obtain the optimal combination of tile representations so that we can transmit the omnidirectional video to users with satisfactory quality but low bitrate. The experimental results demonstrate the effectiveness of our proposed approach when it is applied to omnidirectional video streaming.
Funding Information
  • National Natural Science Foundation of China (U20A20184)
  • National Natural Science Foundation of China (62031009)
  • National Natural Science Foundation of China (62001259)
  • China Postdoctoral Science Foundation (2022M711731)

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