Abstract:
The Quality of Experience (QoE) metric is used as a direct evaluation of customers' experiences in video streaming diffusion, which is
very important for network management, especially for the optimization and the improvement of the network. Hence, it is important to
continuously quantify the perceived QoE of streaming video clients to
minimize the QoE degradation. Nevertheless, the continuous evaluation of QoE is challenging as it is determined by complex dynamic
interactions among the QoE influencing factors. Thus, in this work, a
new Deep Incremental Support Vector Machine (ISVM) QoE assessment model is developed that integrates deep learning techniques and
a multiclass ISVM. The deep learning layer is employed to extract
deep features which have discriminative power and lead to performance improvement. ISVM algorithm aims to manage non-stationary
and massive amounts of data in real-time scenarios. Experiments are
carried out on a real-world public datasets. The findings show that
our model outperforms the state-of-the-art models for QoE evaluation.
