Network Fortune Cookie: Using Network Measurements to Predict Video Streaming Performance and QoE

Abstract

Due to the fact that video streaming is the current "killer" application and for competitiveness, telecommunication service providers need to be able to answer a fundamental question: to which extent is the available network infrastructure able to successfully provide users with a satisfactory experience when running video streaming applications? Answering this question is far from trivial because existing techniques are neither scalable nor accurate enough. To address this issue, we propose a model to predict video streaming quality based on the observation of performance indicators of the underlying IP network. To accomplish this objective, the proposed model - created using LTE networks as case study - leverages low network consumption active measurements and machine learning techniques. Obtained results show that the proposed solution produces accurate estimates (average error of less than 10%) while keeping intrusiveness around twenty times lower than traditional techniques.