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

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Publisher: SMPTE — White Plains, NY, USA
Doc Type: Journal Article
Content Type: Original Research
Abbreviated Title: SMPTE Mot. Imag. J
Volume: 131, No. 4, pp. 45–49
Abstract: 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.
Publication Date: 2022-05-01
DOI: 10.5594/JMI.2022.3160832
ISSN: Print: 1545-0279 | Electronic: 2160-2492
Link: https://doi.org/10.5594/JMI.2022.3160832
Author(s): Abdelmajid Moussaoui
bio
Abdelmajid Moussaoui received an engineering degree in multimedia networking from Telecom-Paris, Paris, France, in 2020. He is a research engineer in the Research and Innovation Department of Ateme, Vélizy, France. His current research areas are video encoding, codec orchestration in the cloud, and machine learning. He holds several pending patents related to the optimization of the orchestration of live video channels distribution in the cloud.
Mickaël Raulet
bio
Mickaël Raulet is the chief technology officer at Ateme, Vélizy, France, where he drives research and innovation with various collaborative research and development projects. He represents Ateme in several standardization bodies: Advanced Television Systems Committee (ATSC), Digital Video Broadcasting (DVB), 3rd Generation Partnership Project (3GPP), International Organization for Standardization / International Electrotechnical Commission (ISO/IEC), International Telecommunication Union (ITU), Moving Picture Experts Group (MPEG), Dynamic Adaptive Streaming over HTTP Industry Forum (DASH-IF), Common Media Application Format- Industry Forum (CMAF-IF), Streaming Video Alliance (SVA), and UHDForum. He is the author of numerous patents and more than 100 conference papers and journal scientific articles. In 2006, he received a PhD from INSA, Rennes, France, in electronic and signal processing, in collaboration with Mitsubishi Electric ITE, Rennes, France.
Thomas Guionnet
bio
Thomas Guionnet is a fellow research engineer at Ateme, Vélizy, France, where he currently leads the innovation team’s research on artificial intelligence applied to video compression. Beyond his work for Ateme, he has also contributed to the International Organization for Standardization / Moving Picture Experts Group (ISO/MPEG) - International Electrotechnical Commission Telecommunication Standardization Sector / Video Coding Experts Group (ITU-T/VCEG) - versatile video coding (VVC), HEVC, and high efficiency video coding- 3 dimensions (HEVC-3D) standardization process; he teaches video compression at the ESIR Engineering School, Rennes, France; and he has authored numerous publications including patents, international conference papers, and journal articles. Prior to joining Ateme, he spent 10 years at Envivio conducting research on real-time encoding, video-preprocessing, and video quality assessment. He holds a PhD from Rennes 1 University, Rennes.
Keyword(s): Artificial Intelligence, Cloud, Content-Adaptive Encoding, HEVC, Kubernetes, Machine Learning, Video Coding, Video Compression, Video Quality
Copyright: © 2022 SMPTE

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Abdelmajid Moussaoui, Mickaël Raulet, and Thomas Guionnet; Dynamic Seamless Resource Allocation for Live Video Compression on a Kubernetes Cluster, SMPTE Motion Imaging Journal ( Volume: 131, Issue: 4, May 2022); SMPTE, 2022. Available at https://doi.org/10.5594/JMI.2022.3160832

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Abdelmajid Moussaoui, Mickaël Raulet, and Thomas Guionnet; Dynamic Seamless Resource Allocation for Live Video Compression on a Kubernetes Cluster, SMPTE Motion Imaging Journal ( Volume: 131, Issue: 4, May 2022); SMPTE, 2022. Available at https://doi.org/10.5594/JMI.2022.3160832

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<span class="citation">Abdelmajid Moussaoui, Mickaël Raulet, and Thomas Guionnet; <cite>Dynamic Seamless Resource Allocation for Live Video Compression on a Kubernetes Cluster</cite>, SMPTE Motion Imaging Journal ( Volume: 131, Issue: 4, May 2022); SMPTE, 2022. Available at <a href="https://doi.org/10.5594/JMI.2022.3160832" target="_blank" rel="noopener">https://doi.org/10.5594/JMI.2022.3160832</a></span>

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Abdelmajid Moussaoui, Mickaël Raulet, and Thomas Guionnet; Dynamic Seamless Resource Allocation for Live Video Compression on a Kubernetes Cluster, SMPTE Motion Imaging Journal ( Volume: 131, Issue: 4, May 2022); SMPTE, 2022
doi: 10.5594/JMI.2022.3160832
url: https://doi.org/10.5594/JMI.2022.3160832

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Abdelmajid Moussaoui, Mickaël Raulet, and Thomas Guionnet; <cite id="bib-10-5594-jmi-2022-3160832">Dynamic Seamless Resource Allocation for Live Video Compression on a Kubernetes Cluster</cite>, SMPTE Motion Imaging Journal ( Volume: 131, Issue: 4, May 2022); SMPTE, 2022
<span class="doi">10.5594/JMI.2022.3160832</span>
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