  A rapid increase in the video traffic together with an increasing demand for
higher quality videos has put a significant load on content delivery networks
in the recent years. Due to the relatively limited delivery infrastructure, the
video users in HTTP streaming often encounter dynamically varying quality over
time due to rate adaptation, while the delays in video packet arrivals result
in rebuffering events. The user quality-of-experience (QoE) degrades and varies
with time because of these factors. Thus, it is imperative to monitor the QoE
continuously in order to minimize these degradations and deliver an optimized
QoE to the users. Towards this end, we propose a nonlinear state space model
for efficiently and effectively predicting the user QoE on a continuous time
basis. The QoE prediction using the proposed approach relies on a state space
that is defined by a set of carefully chosen time varying QoE determining
features. An evaluation of the proposed approach conducted on two publicly
available continuous QoE databases shows a superior QoE prediction performance
over the state-of-the-art QoE modeling approaches. The evaluation results also
demonstrate the efficacy of the selected features and the model order employed
for predicting the QoE. Finally, we show that the proposed model is completely
state controllable and observable, so that the potential of state space
modeling approaches can be exploited for further improving QoE prediction.
