# Estimating PQoS of Video Conferencing on Wi-Fi Networks Using Machine Learning
## 4 Discussion  
This study introduced an approach for estimating the PQoS of video conferencing calls over Wi-Fi networks using 802.11-specific network performance parameters, while achieving a classification accuracy of more than 92%. The results of this study indicated that 802.11-specific network performance parameters on Wi-Fi APs highly correlate with the user-perceived quality of video conferencing calls in homes and buildings. Reviewing the literature indicated that existing approaches perform quality assessments and notify service providers regarding only levels of quality. However, existing approaches do not provide additional information about the root cause of quality degradation. Since customers consider service providers responsible for any quality degradation, they expect customer service to identify these causes and solve the corresponding issues as soon as possible. In this regard, our approach provides a model that is easy for operational teams to understand and use in identifying the root causes of quality degradation on wireless networks. The results demonstrate that our approach achieved higher classification accuracy than the results presented in other studies [3, 17, 18, 19, 20], even though related works have often used binary classification, which is essentially a simpler classification problem than multi-class prediction. Existing approaches have also used external measurements to collect generic network performance parameters for training ML algorithms and identifying quality degradation. In doing so, they increase operational costs for ISPs (shipping measurement equipment to customers, for example) and impose privacy concerns about data analytics. Although a few studies used Wi-Fi as an access network, related works did not leverage 802.11-specific network performance parameters for estimating perceived \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{3}{c}{**J48**} & \multicolumn{3}{c}{**LMT**} & \multicolumn{3}{c}{**REPTree**} & \multicolumn{3}{c}{**NBTree**} & \multicolumn{3}{c}{**RIDOR**} & \multicolumn{3}{c}{**MLP**} \\ \cline{2-19}  & **A** & **I** & **C** & **A** & **I** & **C** & **A** & **I** & **C** & **A** & **I** & **C** & **A** & **I** & **C** & **A** & **I** & **C** \\ \hline
2n & + & + & + & + & + & + & + & + & + & + & + & \(-\) & + & \(-\) & + & + & \(-\) & \(-\) \\
2 ax & + & + & \(-\) & + & + & \(-\) & + & + & + & + & + & \(-\) & + & \(-\) & + & \(+\) & \(-\) & \(-\) \\
5 ac & + & + & + & + & + & + & + & + & + & + & + & \(-\) & + & \(-\) & + & \(+\) & \(-\) & \(-\) \\
5 ax & + & + & + & + & + & + & + & + & + & + & + & \(-\) & + & \(-\) & + & \(+\) & \(-\) & \(-\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overall assessment of ML algorithms with respect to accuracy (A), interpretability (I), and computational cost (C) criteria. quality. Although related works trained their ML algorithms with relatively small datasets, this study produced multiple large datasets, with nearly 40,000 instances in total, to enhance the generalization of the output model. In contrast to related works, this study proposed a novel method in the form of edge computing to address the shortcomings of existing approaches. Most notably, this study pioneered estimating the perceived quality of video conferencing services using 802.11-specific network performance parameters for off-the-shelf Wi-Fi APs. Although ISPs promote APs operating at recent 802.11 ac/ax standards to improve quality of wireless networks, testing video conferencing in these networks indicated that devices in these networks may experience bad quality even more than devices operating at 802.11n standards in various wireless conditions. In addition, these tests demonstrated that our methodology can assess perceived quality of recent Wi-Fi technologies and does not require modification. In addition, this study used RTT, jitter, packet loss, and number of packets received to calculate PQoS in the form of MOS values for each instance in the datasets.