Dynamic Seamless Resource Allocation for Live Video Compression on a Kubernetes Cluster

A solution is proposed on top of Kubernetes to dynamically allocate services resources without service interruption. It serves as the basis for optimizing a live video compression service. It is demonstrated that dynamic resource allocation can benefit to a video compression application, either by reducing the resource consumption, hence costs, or by enhancing delivered video quality. By combining the proposed solution with an elastic encoder and machine learning for content complexity estimation, a content and application aware dynamic resource orchestrator for real-time video compression is designed. Preliminary experimental results using ATEME Titan Live Microservices [8] encoders demonstrate substantial bitrate reductions on even the most demanding channel.

Published

2021-11

Content type

Original Research

Keywords

Video compression, machine learning, AI, Cloud, Kubernetes, Video Coding, HEVC, Video quality, Content Adaptive

DOI

10.5594/M001951