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

Autori
Ammar, Doreid ; De Moor, Katrien ; Skorin-Kapov, Lea ; Fiedler, Markus ; Heegaard, Poul E.

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of the IEEE 44th Conference on Local Computer Networks (LCN) / - : Institute of Electrical and Electronics Engineers (IEEE), 2019, 406-413

ISBN
978-1-7281-1028-8

Skup
IEEE 44th Conference on Local Computer Networks (LCN)

Mjesto i datum
Osnabrück, Njemačka, 14.10.2019. - 17.10.2019

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Quality of Experience (QoE) ; WebRTC ; machine learning ; video-blockiness ; audio distortion

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo

POVEZANOST RADA