Content-gnostic Bitrate Ladder Prediction for Adaptive Video Streaming

Abstract

A challenge that many video providers face is the heterogeneity of networks and display devices for streaming, as well as dealing with a wide variety of content with different encoding performance. In the past, a fixed bit rate ladder solutionbased on a ”fitting all” approach has been employed. However, such a content-tailored solution is highly demanding; the computational and financial cost of constructing the convex hull per video by encoding at all resolutions and quantization levels is huge. In this paper, we propose a content-gnostic approachthat exploits machine learning to predict the bit rate rangesfor different resolutions. This has the advantage of significantlyreducing the number of encodes required. The first results, based on over 100 HEVC-encoded sequences demonstrate the potential, showing an average Bjøntegaard Delta Rate (BDRate) loss of 0.51% and an average BDPSNR loss of 0.01 dB compared to the ground truth, while significantly reducing the number of pre-encodes required when compared to two other methods (by 81%-94%).