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Exploring the Usefulness of Machine Learning in the Context of WebRTC Performance Estimation
EMLYON Business School, FRA.
Norges Teknisk-Naturvitenskapelige Universitet, NOR.
University of Zagreb, HRV.
Blekinge Institute of Technology, Faculty of Computing, Department of Technology and Aesthetics.
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2019 (English)In: Proceedings - Conference on Local Computer Networks, LCN / [ed] Andersson K.,Tan H.-P.,Oteafy S., IEEE Computer Society , 2019, p. 406-413Conference paper, Published paper (Refereed)
Abstract [en]

We address the challenge faced by service providers in monitoring Quality of Experience (QoE) related metrics for WebRTC-based audiovisual communication services. By extracting features from various application-layer performance statistics, we explore the potential of using machine learning (ML) models to estimate perceivable quality impairments and to identify root causes. We argue that such performance-related data can be valuable and informative from a QoE assessment point of view, by allowing to identify the party/parties in a call that is/are experiencing quality impairments, and to trace the origins and causes of the problem. The paper includes case studies of multi-party videoconferencing that are established in a laboratory environment and exposed to various network disturbances and CPU limitations. Our results show that perceivable quality impairments in terms of video blockiness and audio distortions may be estimated with a high level of accuracy, thus proving the potential of exploiting ML models for automated QoE-driven monitoring and estimation of WebRTC performance. © 2019 IEEE.

Place, publisher, year, edition, pages
IEEE Computer Society , 2019. p. 406-413
Keywords [en]
audio distortion, machine learning, Quality of Experience (QoE), video-blockiness, WebRTC, Computer networks, Learning systems, Video conferencing, Audiovisual communication services, Blockiness, Laboratory environment, Network disturbances, Performance estimation, Performance statistics, Quality of service
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-19319DOI: 10.1109/LCN44214.2019.8990677Scopus ID: 2-s2.0-85080932466ISBN: 9781728110288 (print)OAI: oai:DiVA.org:bth-19319DiVA, id: diva2:1414873
Conference
44th Annual IEEE Conference on Local Computer Networks, LCN 2019, 14 October 2019 through 17 October 2019
Available from: 2020-03-16 Created: 2020-03-16 Last updated: 2020-03-18Bibliographically approved

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Fiedler, Markus

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Citation style
  • apa
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