Master data

Title: QoE- and Energy-aware Content Consumption For HTTP Adaptive Streaming
Subtitle:
Abstract:

Video streaming services account for the majority of today's traffic on the Internet, and according to recent studies, this share is expected to continue growing. Given this broad utilization, research in video streaming is recently moving towards energy-aware approaches, which aim at reducing the energy consumption of the devices involved in the streaming process. On the other side, the perception of quality delivered to the user plays an important role, and the advent of HTTP Adaptive Streaming (HAS) changed the way quality is perceived. The focus is not any more exclusively on the Quality of Service (QoS) but rather oriented towards the Quality of Experience (QoE) of the user taking part in the streaming session. Therefore video streaming services need to develop Adaptive BitRate (ABR) techniques to deal with different network conditions on the client side or appropriate end-to-end strategies to provide high QoE to the users. The scope of this doctoral study is within the end-to-end environment with a focus on the end-users domain, referred to as the player environment, including video content consumption and interactivity. This thesis aims to investigate and develop different techniques to increase the delivered QoE to the users and minimize the energy consumption of the end devices in HAS context. We present four main research questions to target the related challenges in the domain of content consumption for HAS systems.

Keywords: Multi-codec, HTTP/3, machine learning, green computing, HAS
Publication type: Article in Proceedings (Authorship)
Publication date: 07.06.2023 (Print)
Published by: MMSys '23 Proceedings of the 14th Conference on ACM Multimedia Systems
MMSys '23 Proceedings of the 14th Conference on ACM Multimedia Systems
to publication
 ( ACM Digital Library; )
Title of the series: -
Volume number: -
First publication: Yes
Version: -
Page: pp. 348 - 352

Versionen

Keine Version vorhanden
Publication date: 07.06.2023
ISBN:
  • 979-8-4007-0148-1
ISSN: -
Homepage: https://dl.acm.org/doi/abs/10.1145/3587819.3593029
Publication date: 08.06.2023
ISBN (e-book): -
eISSN: -
DOI: http://dx.doi.org/10.1145/3587819.3593029
Homepage: https://dl.acm.org/doi/abs/10.1145/3587819.3593029
Open access
  • Available online (not open access)

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 Systeme

Cooperations

No partner organisations selected

Articles of the publication

No related publications