# 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 802.11 ac/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. perceived quality of service (PQoS); machine learning; Wi-Fi; video conferencing; WebRTC; quality of experience (QoE) +
Footnote †: journal: _Future Internet_  
Recognizing this growing dissatisfaction, ISPs have sought to monitor quality of service on customer premises, generally using external measurement approaches, such as packet capture and speed tests. However, these approaches impose excessive operational costs and may further inconvenience customers. Moreover, speed test approaches only measure end-to-end Internet bandwidth and quality and therefore cannot capture transient wireless issues. As a result, users may remain frustrated even after speed tests. The fact that users are more concerned with the quality of video conferencing than with the maximum bandwidth or end-to-end performance parameters further highlights the need for monitoring the user-perceived quality of service (PQoS) of video conferencing. Traditionally, service providers have requested user feedback by distributing surveys or embedding them in the application interface after video conferencing to evaluate quality of experience (QoE). However, QoE tends to represent a user's general opinion and is influenced by non-technical factors, including social expectations and the price of the service. Hence, analyzing the impact of technical factors on perceived quality is preferable to feedback campaigns for assessing the quality of delivered services. Various research projects present objective approaches for estimating the level of service acceptability on Wi-Fi networks. However, while the majority of studies employ the term QoE to estimate service acceptability, they consider only technical factors, such as network performance parameters. For example, [PERSON] et al. used a subset of Wi-Fi network performance parameters, structural similarity index (SSIM) of video real-time communication (RTC) and perceptual evaluation of speech quality (PESQ) of audio RTC, to calculate QoE in the form of mean opinion score (MOS) values [3]. In the same way, [PERSON] et al.