Deep reinforced bitrate ladders for adaptive video streaming

Abstract

In the typical transcoding pipeline for adaptive video streaming, raw videos are pre-chunked and pre-encoded according to a set of resolution-bitrate or resolution-quality pairs on the server-side, where the pair is often named as bitrate ladder. Different from existing heuristics, we argue that a good bitrate ladder should be optimized by considering video content features, network capacity, and storage costs on the cloud. We propose DeepLadder, a per-chunk optimization scheme which adopts state-of-the-art deep reinforcement learning (DRL) method to optimize the bitrate ladder w.r.t the above concerns. Technically, DeepLadder selects the proper setting for each video resolution autoregressively. We use over 8,000 video chunks, measure over 1,000,000 perceptual video qualities, collect real-world network traces for more than 50 hours, and invent faithful virtual environments to help train DeepLadder efficiently. Across a series of comprehensive experiments on both Constant Bitrate (CBR) and Variable Bitrate (VBR)-encoded videos, we demonstrate significant improvements in average video quality bandwidth utilization, and storage overhead in comparison to prior work as well as the ability to be deployed in the real-world transcoding framework.