Network side lightweight and scalable YouTube QoE estimation

YouTube is a prominent Over The Top (OTT) application increasingly used on mobile devices for watching online videos. Due to the mobile environment, cellular technology specific reasons and the delivery mechanism adopted by YouTube, providing seamless customer experience is not straightforward. Being a popular application whose availability is important from customer satisfaction point of view, operators must deploy mechanisms capable of network wide YouTube QoE measurement and degradation detection. Known solutions aim to provide a comprehensive set of QoE influence factors such as the exact stallings for each video session. However, in order to generate network wide insight to a statistically relevant set of all YouTube downloads, such mechanisms may not represent an optimal balance between the provided level of details and their cost or complexity. Instead, this paper proposes a lightweight, real time, network side QoE estimation method suitable for network wide deployment. The proposed solution is able to detect YouTube videos with QoE degradations through novel metrics that were validated with real network measurements. The paper also presents the statistical analysis of a large set of videos showing how the method can evaluate the YouTube QoE in a network.