Towards estimating video QoE based on frame loss statistics of the video streams

This paper describes a unique path of estimating the Quality of Experience (QoE) for streaming video. Instead of following the widely researched idea of correlating video degradation with Quality of Service (QoS) metrics of the network, we propose to merely analyze the nature of packet losses. The majority of today's streaming video traffic is using compressed formats, where the videos are composed from a series of key, predicted, and bi-predictive frames (I frame, P frame, B frame, respectively). The content of these frames gets packetized, and sent over the network as video traffic. Losing packets that belong to I, P and B frames lead to QoE degradations of different severity. The degrading effect of these packet losses depend on the type of the video (two extremes can be slowly moving balloons versus fireworks in the sky), the type of frame, the volume of the lost packets, and further factors. The aim of this paper is to introduce a measurement and analysis method for correlating packet loss patterns of key, predicted and bi-predictive frames with objective quality metrics, such as the SSIM (Structural SIMilarity index). Furthermore, measurement results on the effect of such packet losses for various video types are also presented in this paper.