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Exploring the Usefulness of Machine Learning in the Context of WebRTC Performance Estimation

Ammar, Doreid; De Moor, Katrien; Skorin-Kapov, Lea; Fiedler, Markus; Heegaard, Poul Einar
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Ammar (686.2Kb)
Permanent lenke
https://hdl.handle.net/11250/2648020
Utgivelsesdato
2019
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  • Institutt for informasjonssikkerhet og kommunikasjonsteknologi [1955]
  • Publikasjoner fra CRIStin - NTNU [26671]
Originalversjon
10.1109/LCN44214.2019.8990677
Sammendrag
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.
Utgiver
Institute of Electrical and Electronics Engineers (IEEE)

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