Publication: CSDN: CDN-Aware QoE Optimization in SDN...
Master data
Title: | CSDN: CDN-Aware QoE Optimization in SDN-Assisted HTTP Adaptive Video Streaming |
Subtitle: | |
Abstract: | Recent studies have revealed that network-assisted techniques, by providing a comprehensive view of the network, improve HTTP Adaptive Streaming (HAS) system performance significantly. This paper leverages the capability of Software-Defined Networking, Network Function Virtualization, and edge computing to introduce a CDN-Aware QoE Optimization in SDN-Assisted Adaptive Video Streaming (CSDN) framework. We employ virtualized edge entities to collect various information items and run an optimization model with a new server/segment selection approach in a time-slotted fashion to serve the clients’ requests by selecting optimal cache servers. In case of a cache miss, a client’s request is served by an optimal replacement quality from a cache server, by a quality transcoded from an optimal replacement quality at the edge, or by the originally requested quality from the origin server. Comprehensive experiments conducted on a large-scale testbed demonstrate that CSDN outperforms other approaches in terms of the users’ QoE and network utilization. |
Keywords: | Dynamic Adaptive Streaming over HTTP (DASH), Edge Computing, Network-Assisted Video Streaming, Quality of Experience (QoE), Software Defined Networking (SDN), Network Function Virtualization (NFV), Video Transcoding, Content Delivery Network (CDN) |
Publication type: | Article in Proceedings (Authorship) |
Publication date: | 04.10.2021 (Print) |
Published by: |
LCN '21 Proceedings of the IEEE 46th Conference on Local Computer Networks
LCN '21 Proceedings of the IEEE 46th Conference on Local Computer Networks
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IEEE;
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to publication |
Title of the series: | - |
Volume number: | - |
First publication: | Yes |
Version: | - |
Page: | pp. 525 - 532 |
Versionen
Keine Version vorhanden |
Publication date: | 07.09.2021 |
ISBN (e-book): | - |
eISSN: | - |
DOI: | http://dx.doi.org/10.1109/lcn52139.2021.9524970 |
Homepage: | - |
Open access |
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Publication date: | 04.10.2021 |
ISBN: |
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ISSN: | 0742-1303 |
Homepage: | https://ieeexplore.ieee.org/document/9524970 |
Authors
Reza Farahani (internal) |
Farzad Tashtarian (internal) |
Hadi Amirpourazarian (internal) |
Christian Timmerer (internal) |
Mohammad Ghanbari (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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Research Cluster | No research Research Cluster selected |
Peer reviewed |
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