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

A solution is proposed on top of Kubernetes to dynamically allocate service resources without service interruption. It is the basis for optimizing a live video compression service. It is demonstrated that dynamic resource allocation can benefit 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 (ML) for content complexity estimation, a content- and application-aware dynamic resource orchestrator for realtime video compression is designed. Preliminary experimental results using ATEME Titan Live Micro-Services¹encoders demonstrate substantial bitrate reductions on even the most demanding channel.

Print ISSN

1545-0279

Electronic ISSN

2160-2492

Published

2022-05

Content type

Original Research

Keywords

AI, cloud, content adaptive, high efficiency video coding (HEVC), Kubernetes, machine learning (ML), video coding, video compression, video quality

DOI

10.5594/JMI.2022.3160832