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

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.

Comparison of popular video conferencing apps using client-side measurements on different backhaul networks

“…Similar to our approach, they used structural similarity to estimate QoE. Morshedi and Noll [18] perform perceived QoS measurement in the WiFi access points in customers' premises. They used machine learning to develop models using which ISPs may estimate the QoS on their networks.…”

Wireless Internet, Multimedia, and Artificial Intelligence: New Applications and Infrastructures

“…An example of this is the work in [1], where the authors present an innovative approach for damage detection of infrastructures on-edge devices by exploiting a braininspired algorithm, or in [2], where, instead, two techniques aimed at supporting the user in making privacy choices about sharing personal content online have been proposed by the authors. Another interesting work, which aimed 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, has been proposed in [3].…”