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
to publication
 ( IEEE; )
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
  • Available online (not open access)
Publication date: 04.10.2021
ISBN:
  • 978-1-6654-1886-7
  • 978-1-6654-4800-0
ISSN: 0742-1303
Homepage: https://ieeexplore.ieee.org/document/9524970

Assignment

Organisation Address
Fakultät für Technische Wissenschaften
 
Institut für Informationstechnologie
Universitaetsstr. 65-67
9020 Klagenfurt am Wörthersee
Austria
   martina.steinbacher@aau.at
http://itec.aau.at/
To organisation
Universitaetsstr. 65-67
AT - 9020  Klagenfurt am Wörthersee

Categorisation

Subject areas
  • 1020 - Computer Sciences
Research Cluster No research Research Cluster selected
Peer reviewed
  • Yes
Publication focus
  • Science to Science (Quality indicator: II)
Classification raster of the assigned organisational units:
working groups
  • Multimedia Communication

Cooperations

No partner organisations selected

Articles of the publication

No related publications