Prediction of quality degradation for mobile video streaming apps: A case study using YouTube

Abstract

The growing popularity for developing streaming media applications over HTTP triggers new challenges for managing video quality over mobile devices. Quality of online videos gets significantly affected due to the capacity fluctuations of underlying communication channel, which is very much common for cellular mobile networks. Such fluctuations lead to re-buffering and sudden drops in video quality, adversely affecting video watching experience. In this poster, we propose a light-weight method for early detection of network capacity degradation. We explore the traffic characteristics of mobile streaming video apps, by considering YouTube Android app as a use case. We show that by observing the traffic pattern, we can predict possible video quality degradation and video re-buffering events. We develop a methodology for early prediction of possible re-buffering. The experimental results reveal that our proposed scheme works with very high accuracy.