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Exploring the Usefulness of Machine Learning in the Context of WebRTC Performance Estimation
Emlyon business school, FRA.
NTNU - Norwegian University of Science and Technology, NOR.
University of Zagreb, HRV.
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för teknik och estetik.ORCID-id: 0000-0001-8929-4911
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2019 (Engelska)Ingår i: Proceedings - Conference on Local Computer Networks, LCN / [ed] Andersson K.,Tan H.-P.,Oteafy S., IEEE Computer Society , 2019, s. 406-413Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
IEEE Computer Society , 2019. s. 406-413
Nyckelord [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
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
URN: urn:nbn:se:bth-19319DOI: 10.1109/LCN44214.2019.8990677ISI: 000574771800067Scopus ID: 2-s2.0-85080932466ISBN: 9781728110288 (tryckt)OAI: oai:DiVA.org:bth-19319DiVA, id: diva2:1414873
Konferens
44th Annual IEEE Conference on Local Computer Networks, Osnabruck, 14 October 2019 through 17 October 2019
Ingår i projekt
Bigdata@BTH- Scalable resource-efficient systems for big data analytics, KK-stiftelsen
Forskningsfinansiär
KK-stiftelsen, 2014-0032Tillgänglig från: 2020-03-16 Skapad: 2020-03-16 Senast uppdaterad: 2021-07-30Bibliografiskt granskad

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Fiedler, Markus

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Totalt: 157 träffar
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