LAUSR

With the increasing traffic of Video on Demand (VoD), network providers are seeking to deliver high Quality of Experience (QoE) for their users. Many methods have been proposed to assess VoD-related QoE. Some of them rely on client instrumentation and reporting QoE information to network elements, such as Server and Network Assisted DASH, others are based on statistical methods that make QoE inferences using monitored network conditions, such as throughput and delays. In this article, we present a practical method to estimate QoE for VoD using the widely supported Internet Control Message Protocol (ICMP) probes. Measured network conditions are used as input to a Machine Learning (ML) model that estimates QoE in terms of Mean Opinion Score (MOS), based on the ITU-T P.1203 Recommendation. The estimation encompasses video quality switches and playback stalls. We estimate MOS with an average Root Mean Square Error (RMSE) of 1.05 for a catalog of 25 different videos, training a model with sessions of the shortest video, and evaluating the generalization to the full catalog. We performed experiments using a virtualized setup as well as in a Wide Area Network.