@inproceedings{aea255a7ce85467fa45d130df8849c8c,
title = "Exploring the Usefulness of Machine Learning in the Context of WebRTC Performance Estimation",
abstract = "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.",
keywords = "Quality of Experience (QoE), WebRTC, audio distortion, machine learning, video-blockiness",
author = "Doreid Ammar and Moor, {Katrien De} and Lea Skorin-Kapov and Markus Fiedler and Heegaard, {Poul E.}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 44th Annual IEEE Conference on Local Computer Networks, LCN 2019 ; Conference date: 14-10-2019 Through 17-10-2019",
year = "2019",
month = oct,
doi = "10.1109/LCN44214.2019.8990677",
language = "English",
series = "Proceedings - Conference on Local Computer Networks, LCN",
publisher = "IEEE Computer Society",
pages = "406--413",
editor = "Karl Andersson and Hwee-Pink Tan and Sharief Oteafy",
booktitle = "Proceedings of the 44th Annual IEEE Conference on Local Computer Networks, LCN 2019",
}