Estimating PQoS of Video Conferencing on Wi-Fi Networks Using Machine Learning

Author:

Morshedi MaghsoudORCID,Noll Josef

Abstract

Video conferencing services based on web real-time communication (WebRTC) protocol are growing in popularity among Internet users as multi-platform solutions enabling interactive communication from anywhere, especially during this pandemic era. Meanwhile, Internet service providers (ISPs) have deployed fiber links and customer premises equipment that operate according to recent 802.11ac/ax standards and promise users the ability to establish uninterrupted video conferencing calls with ultra-high-definition video and audio quality. However, the best-effort nature of 802.11 networks and the high variability of wireless medium conditions hinder users experiencing uninterrupted high-quality video conferencing. This paper presents a novel approach to estimate the perceived quality of service (PQoS) of video conferencing using only 802.11-specific network performance parameters collected from Wi-Fi access points (APs) on customer premises. This study produced datasets comprising 802.11-specific network performance parameters collected from off-the-shelf Wi-Fi APs operating at 802.11g/n/ac/ax standards on both 2.4 and 5 GHz frequency bands to train machine learning algorithms. In this way, we achieved classification accuracies of 92–98% in estimating the level of PQoS of video conferencing services on various Wi-Fi networks. To efficiently troubleshoot wireless issues, we further analyzed the machine learning model to correlate features in the model with the root cause of quality degradation. Thus, ISPs can utilize the approach presented in this study to provide predictable and measurable wireless quality by implementing a non-intrusive quality monitoring approach in the form of edge computing that preserves customers’ privacy while reducing the operational costs of monitoring and data analytics.

Funder

Norges Forskningsråd

Publisher

MDPI AG

Subject

Computer Networks and Communications

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Advancing WebRTC QoE Assessment with Machine Learning in Real-World Wi-Fi Scenarios;2024 IEEE International Mediterranean Conference on Communications and Networking (MeditCom);2024-07-08

2. Exploring Data Ownership in Web 2.0 and Web 3.0 with the Integration of Blockchain Technology;Communications in Computer and Information Science;2024

3. Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance With Machine Learning;IEEE Communications Surveys & Tutorials;2022

4. Wireless Internet, Multimedia, and Artificial Intelligence: New Applications and Infrastructures;Future Internet;2021-09-21

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