Abstract
End users stream video increasingly from live broadcasters (via YouTube Live, Twitch etc.). Adaptive live video streaming is realised by transcoding different representations of the original video content. Management of transcoding resources creates costs for the service provider, because transcoding is a CPU-intensive task. Additionally, the content must be transcoded within real time with the transcoding resources in order to provide satisfying Quality of Service. The contribution of this paper is validation of an online architecture for enabling live video transcoding with Docker in a Kubernetes-based cloud environment. Particularly, online cloud resource allocation has been focused on by executing experiments in several configurations. The results indicate that Random Forest regressor provided the best overall performance in terms of precision regarding transcoding speed and CPU consumption on resources, and the amount of realised transcoding tasks. Reinforcement Learning provided lower performance, and required more effort in terms of training.
Highlights
Video content is provided to consumers with Content Delivery Networks (CDN)
The predictors were validated with a cloud system, which consisted of 20 homogeneous Docker containers, while we focused on the allocation of heterogeneous virtual machine (VM) for live video transcoding
Central Processing Unit (CPU) cores had to be allocated separately for each transcoding task based on the target resolution, and VM
Summary
Video content is provided to consumers with Content Delivery Networks (CDN). In order to provide video streams with high quality to end users, the original video has to be transcoded for delivery via the CDN. Transcoding is a CPU-intensive process, in which several representations of the original video are created. The end users adaptively switch between the available video representations due to the variable conditions of the (wireless) network. In live streaming cloud resources have to be capable of providing real time speed of transcoding. Video transcoding as a CPU-intensive task requires powerful computing resources to be utilised. Several commercial companies (e.g. Encoding.com [1], Wowza Media System [2], Bitmovin [3]) provide services for live video transcoding. Machine learned models [4] have been used for improving efficiency of transcoding with cloud resources, and some of the approaches [5,6,7,8,9,10] have
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