Measuring YouTube QoE with ITU-T P.1203 Under Constrained Bandwidth Conditions

Abstract

The available Internet bandwidth has a strong impact on the Quality of Experience of video services. In order to manage their network efficiently and prevent customer churn, Internet Service Providers need to constantly monitor the QoE of video services such as YouTube. However, they often only rely on simple measurement scenarios that consider only one video being loaded repeatedly. In this paper we compare this scenario against a new approach in which multiple videos are being loaded in a session, thereby simulating user behavior. Using a testbed, we study the impact of download speeds on Key Performance Indicators (KPIs such as initial loading time and stalling events) and user QoE as measured using the ITU-T P.1203 standard. We show that the monitoring paradigm has a significant impact on the obtained results. We further provide a prediction model for estimating the impact of download speed on KPIs and user QoE.