Mobile network operators collect a humongous amount of network measurements. Among those, sector Key Performance Indicators (KPIs) are used to monitor the radio access, i.e., the "last mile" of mobile networks. Thresholding mechanisms and synthetic combinations of KPIs are used to assess the network health, and rank sectors to identify the underperforming ones. It follows that the available monitoring methodologies heavily rely on the fine grained tuning of thresholds and weights, currently established through domain knowledge of both vendors and operators. In this paper, we study how to bridge sector KPIs to reflect Quality of Experience (QoE) groundtruth measurements, namely throughput, latency and video streaming stall events. We leverage one month of data collected in the operational network of mobile network operator serving more than 10 million subscribers. We extensively investigate up to which extent adopted methodologies efficiently capture QoE. Moreover, we challenge the current state of the art by presenting data-driven approaches based on Particle Swarm Optimization (PSO) metaheuristics and random forest regression algorithms, to better assess sector performance. Results show that the proposed methodologies outperforms state of the art solution improving the correlation with respect to the baseline by a factor of 3, and improving visibility on underperforming sectors. Our work opens new areas for research in monitoring solutions for enriching the quality and accuracy of the network performance indicators collected at the network edge.

Video streaming on mobile devices is prone to a multitude of faults and although well established video Quality of Experience (QoE) metrics such as stall frequency are a good indicator of the problems perceived by the user, they do not provide any insights about the nature of the problem nor where it has occurred. Quantifying the correlation between the aforementioned faults and the users' experience is a challenging task due the large number of variables and the numerous points-of-failure.

In this paper, we analyze the YouTube service and the traffic generated from its usage. The purpose of this study is to identify by strictly using passive measurements the information that can be used as metrics or indicators of the progress of individual video sessions and to estimate the impact of these metrics in the user experience. We find a novel method to track the progress of the video playback that, in contrast to previous works, does not require instrumentation of the video player neither browser-based plug-ins. Instead, we extract important statistical information about the status of the playback by reverse engineering the metrics in related HTTP requests that are generated during playback. For the purpose of collecting these metrics, a tool was developed to perform YouTube traffic measurements by means of passive network monitoring in a large university campus network. The analysis of the obtained data revealed the most important sources of initial delay in the sessions as well as buffer outage events and download rate statistics. Further analysis revealed the impact of video advertisements and re-buffering events on the user experience in terms of video abandonment rate.