A QoE Inference Method for DASH Video Using ICMP Probing

Abstract

An increase of Video on Demand (VoD) consumption has occurred in recent years. Delivering high Quality of Experience (QoE) for users consuming VoD is crucial. Many methods were proposed to estimate QoE based on network metrics, or to obtain direct feedback from video players. Recent proposals usually require monitoring tools installed in multiple network nodes, instrumentation of client devices, updates on existing network elements, among others. We propose a method based on Internet Control Message Protocol (ICMP) probing to estimate QoE for users consuming VoD. The method allows network operators to estimate which QoE level can be delivered to the user according to current network conditions using a Machine Learning (ML) model. Our method does not require installation of software at different network nodes, relying on ICMP probing which is widely supported by existing devices. Our QoE inference model estimates Mean Opinion Score (MOS) with Root Mean Square Error (RMSE) of 0.98, with additional 27 Kbps of traffic during probing. We evaluate the model's generalization capacity when estimating QoE for videos different from the one used for training, which can speed up model's creation process. In those cases MOS was estimated with RMSE of 1.01.