Fast multi-encoding to reduce the cost of video streaming

TL;DR

This paper reviews various multi-encoding schemes, including machine learning approaches, to accelerate HEVC video encoding, especially for DASH streaming, by sharing analysis information across representations and leveraging parallel processing.

Contribution

It provides a comprehensive overview of multi-encoding techniques and integrates machine learning methods to improve encoding efficiency in cloud-based environments.

Findings

Sharing analysis metadata reduces encoding time.

Parallel multi-encoding accelerates large-scale video processing.

Machine learning enhances prediction accuracy in encoding.

Abstract

The growth in video Internet traffic and advancements in video attributes such as framerate, resolution, and bit-depth boost the demand to devise a large-scale, highly efficient video encoding environment. This is even more essential for Dynamic Adaptive Streaming over HTTP (DASH)-based content provisioning as it requires encoding numerous representations of the same video content. High Efficiency Video Coding (HEVC) is one standard video codec that significantly improves encoding efficiency over its predecessor Advanced Video Coding (AVC). This improvement is achieved at the expense of significantly increased time complexity, which is a challenge for content and service providers. As various representations are the same video content encoded at different bitrates or resolutions, the encoding analysis information from the already encoded representations can be shared to accelerate the encoding of other representations. Several state-of-the-art schemes first encode a single representation, called a reference representation. During this encoding, the encoder creates analysis metadata with information such as the slicetype decisions, CU, PU, TU partitioning, and the HEVC bitstream itself. The remaining representations, called dependent representations, analyze the above metadata and then reuse it to skip searching some partitioning, thus, reducing the computational complexity. With the emergence of cloud-based encoding services, video encoding is accelerated by utilizing an increased number of resources, i.e., with multi-core CPUs, multiple representations can be encoded in parallel. This paper presents an overview of a wide range of multi-encoding schemes with and without the support of machine learning approaches integrated into the HEVC Test Model (HM) and x265, respectively.