# Crowdsourcing Based Performance Analysis of Mobile User Heterogeneous Services
## 1 Introduction  
Monitoring the Quality of Experience (QoE) nowadays is crucial for the main involved entities in the application service chain: (i) mobile network operators (e.g., Free, Orange and SFR in France), service providers (e.g., Google, Microsoft) and the end-users (e.g., customers or companies). Estimating the QoE in terms of user's Mean Opinion Score (MOS) can help operators and providers to identify performance anomalies and resolve them in order to try to retain their customers [1]. In fact, the measurement techniques at the end-user side for assessing the QoE has attracted the attention of many research works guided by operators, service providers or the academic community because it is an easy and cheap way to collect data at a large scale [2]. In cellular networks, for a given base station (e.g., \(LTE\) eNB), an operator can measure all physical parameters related to all user equipment associated with. However, with mobility, some users may dissociate from the eNB to another one (if exists). In this case, the operator will not be able to keep measuring (monitoring) the perceived QoE for that user and during the complete service run time. In addition, a user may be completely disconnected due to a lack of coverage. Moreover, the trend towards end-to-end encryption (like HTTPS) has significantly reduced the visibility of network operators on the application parameters(buffer information, etc.) and the traffic of their customers, making the monitoring process more challenging and cumbersome [3]. 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. It is also crucial for operators to easily produce coverage maps with performance indicators to demonstrate that the coverage commitments on which the license is conditional have been met in addition to limiting customer churn due to quality dissatisfaction. 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 accuracy of 70%, in addition to average bitrate estimation with an 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. The authors in [7] have built up 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.