Energy-Efficient Multi-Codec Bitrate-Ladder Estimation for Adaptive Video Streaming

TL;DR

This paper presents an online scheme for estimating multi-codec bitrate ladders that significantly reduces energy consumption in adaptive video streaming by removing redundant representations and predicting quality scores.

Contribution

It introduces MCBE, a novel method that minimizes energy use by intelligently selecting bitrate representations across multiple codecs using machine learning.

Findings

Reduces encoding energy by 56.45%

Lowers storage energy by 94.99%

Decreases transmission energy by 77.61%

Abstract

With the emergence of multiple modern video codecs, streaming service providers are forced to encode, store, and transmit bitrate ladders of multiple codecs separately, consequently suffering from additional energy costs for encoding, storage, and transmission. To tackle this issue, we introduce an online energy-efficient Multi-Codec Bitrate ladder Estimation scheme (MCBE) for adaptive video streaming applications. In MCBE, quality representations within the bitrate ladder of new-generation codecs (e.g., High Efficiency Video Coding (HEVC), Alliance for Open Media Video 1 (AV1)) that lie below the predicted rate-distortion curve of the Advanced Video Coding (AVC) codec are removed. Moreover, perceptual redundancy between representations of the bitrate ladders of the considered codecs is also minimized based on a Just Noticeable Difference (JND) threshold. Therefore, random forest-based models predict the VMAF score of bitrate ladder representations of each codec. In a live streaming session where all clients support the decoding of AVC, HEVC, and AV1, MCBE achieves impressive results, reducing cumulative encoding energy by 56.45%, storage energy usage by 94.99%, and transmission energy usage by 77.61% (considering a JND of six VMAF points). These energy reductions are in comparison to a baseline bitrate ladder encoding based on current industry practice.