# Crowdsourcing Based Performance Analysis of Mobile User Heterogeneous Services
## 2 Related Works
They consider video streaming and file downloading services and aim at understanding the dynamic reasoning for adaptive clients in \(5G\) multi-cell wireless scenarios. One important use case of crowdsourcing for Mobile network operators is the estimation of Key Performance Indicators (KPIs) and relevant Key QoE Indicators (KQIs) to quantify the end user's perceived quality. In fact, with the constraints faced for privacy and the adoption of end-to-end encryption, operators do not always have access to these indicators via crowdsourcing. Instead, they appeal to machine-learning models to predict multiple QoE-relevant metrics (KQIs) directly from the analysis of the encrypted traffic as done in [3]. In this context, we can cite the work [4], where the authors provide an estimation system of YouTube video bitrate using a decision tree classifier. In [5], the authors test five ML methods for YouTube video QoE estimation by using a dataset of 1060 video sessions. They found that up to 84% QoE classification accuracy is achieved with the RF method, using only features extracted from encrypted traffic. In [6], the authors introduce another methodology, called eMIMIC, that estimates average bitrate and re-buffering ratio for encrypted video traffic. Three datasets of cellular traces (\(3G\), \(LTE\)) are used. The results indicate that the re-buffering rate is estimated with an accuracy of up to 70%, in addition to average bitrate estimation with error under 100 kbps for up to 80%. Another approach that investigates the estimation of KQI from the physical layer parameters has also attracted some research attention. Authors in [7] have built a QoE model for videos delivered over a radio network (e.g., Long Term Evolution (\(LTE\))) using HTTP (Hypertext Transfer Protocol) adaptive streaming (HAS). Their objective consists of achieving a comparison of the QoE prediction using HAS profile (video presentation) and using radio parameters (physical layer). They concluded that the HAS profile is sufficient and better than the radio scenario parameters to estimate user's QoE in the context of \(LTE\) technology. Based on the same technology, the authors of [8] introduce a no-Reference video streaming QoE estimator by testing different machine learning techniques. The Gradient Tree Boosting (GTB) method is selected to calculate the video QoE using 11 considered radio parameters. This model achieves 78.9% of correlation and 0.114 of MSE. At the end, the authors concluded that the radio parameters related to the transmission rate of the streaming session are the most important features in predicting QoE for the GTB algorithm. In fact, many related works refer to mobile datasets that are produced by either operators without publishing any detail or by research groups but with a reduced number of parameters [13]. In order to have a full detailed dataset in our region and study the performance of the most popular Internet services (e.g., video streaming, downloading and web browsing) over a popular French mobile access network, we decided to build our own dataset. In addition, we would like to study the impact of the radio parameters on the QoE for streaming videos on fixed HD quality rather than using adaptive streaming as done, for example, in [7] in order to evaluate the root cause of poor performance as discussed in detail in Section 5.