Publication: ECAS-ML: Edge Computing Assisted Adapta...
Master data
Title: | ECAS-ML: Edge Computing Assisted Adaptation Scheme with Machine Learning for HTTP Adaptive Streaming |
Subtitle: | |
Abstract: | As the video streaming traffic in mobile networks is increasing, improving the content delivery process becomes crucial, e.g., by utilizing edge computing support. At an edge node, we can deploy adaptive bitrate (ABR) algorithms with a better understanding of network behavior and access to radio and player metrics. In this work, we present ECAS-ML, Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming with Machine Learning. ECAS-ML focuses on managing the tradeoff among bitrate, segment switches and stalls to achieve a higher quality of experience (QoE). For that purpose, we use machine learning techniques to analyze radio throughput traces and predict the best parameters of our algorithm to achieve better performance. The results show that ECAS-ML outperforms other client-based and edge-based ABR algorithms. |
Keywords: | HTTP Adaptive Streaming, Edge computing, Content delivery, Network-assisted video streaming, Quality of experience, Machinge learning |
Publication type: | Article in compilation (Authorship) |
Publication date: | 2022 (Print) |
Published by: |
MMM 2022 Proceedings of the International Conference on Multimedia Modeling
MMM 2022 Proceedings of the International Conference on Multimedia Modeling
(
Springer;
)
to publication |
Title of the series: | Lecture notes in Computer Science (LNCS) |
Volume number: | 13142 |
First publication: | Yes |
Version: | - |
Page: | pp. 394 - 406 |
Versionen
Keine Version vorhanden |
Publication date: | 2022 |
ISBN: |
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ISSN: | 0302-9743 |
Homepage: | https://link.springer.com/chapter/10.1007/978-3-030-98355-0_33 |
Publication date: | 15.03.2022 |
ISBN (e-book): |
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eISSN: | 1611-3349 |
DOI: | http://dx.doi.org/10.1007/978-3-030-98355-0_33 |
Homepage: | https://link.springer.com/chapter/10.1007/978-3-030-98355-0_33 |
Open access |
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Authors
Jesus Aguilar Armijo (internal) |
Ekrem Cetinkaya (internal) |
Christian Timmerer (internal) |
Hermann Hellwagner (internal) |
Assignment
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Fakultät für Technische Wissenschaften
Institut für Informationstechnologie
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AT - 9020 Klagenfurt am Wörthersee |
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Peer reviewed |
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