Cross codec encoding parameter prediction for convex hull-based ABR encoding

Haibo Zhang; Zhijun Lei; Ping-Hao Wu; Gaurang Chaudhari; Wai Lun Tam

doi:10.1117/12.3027748

30 September 2024 Cross codec encoding parameter prediction for convex hull-based ABR encoding

Haibo Zhang, Zhijun Lei, Ping-Hao Wu, Gaurang Chaudhari, Wai Lun Tam

Proceedings Volume 13137, Applications of Digital Image Processing XLVII; 131370X (2024) https://doi.org/10.1117/12.3027748
Event: Optical Engineering + Applications, 2024, San Diego, California, United States

Abstract

In the video streaming service for VOD use case, an important task is to transcode user uploaded videos into multiple encoded bitstreams at different encoding bitrates and encoding resolutions, which allows the client player to leverage ABR (adaptive bitrate) algorithm to select the bitstream segments based on its available bandwidth. In this workflow, the key decision needs to be made is to determine the optimal encoding bitrates and encoding resolutions for every video at each quality or bitrate target in a ABR ladder. To tackle this challenge, an efficient two-stage convex hull based dynamic optimization framework was recently proposed. In this two-stage processing flow, two different encoders, or encoder presets can be used to construct the convex hull to improve the computation efficiency. In this work, we study the cross codec encoding parameter prediction problem in the two-stage system. We first formulate the prediction into an optimization problem, then propose two methods towards this optimization with validation results. We also discuss some potential directions that can further improve the results.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Haibo Zhang, Zhijun Lei, Ping-Hao Wu, Gaurang Chaudhari, and Wai Lun Tam "Cross codec encoding parameter prediction for convex hull-based ABR encoding", Proc. SPIE 13137, Applications of Digital Image Processing XLVII, 131370X (30 September 2024); https://doi.org/10.1117/12.3027748

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