  In video streaming services, predicting the continuous user's quality of
experience (QoE) plays a crucial role in delivering high quality streaming
contents to the user. However, the complexity caused by the temporal
dependencies in QoE data and the non-linear relationships among QoE influence
factors has introduced challenges to continuous QoE prediction. To deal with
that, existing studies have utilized the Long Short-Term Memory model (LSTM) to
effectively capture such complex dependencies, resulting in excellent QoE
prediction accuracy. However, the high computational complexity of LSTM, caused
by the sequential processing characteristic in its architecture, raises a
serious question about its performance on devices with limited computational
power. Meanwhile, Temporal Convolutional Network (TCN), a variation of
convolutional neural networks, has recently been proposed for sequence modeling
tasks (e.g., speech enhancement), providing a superior prediction performance
over baseline methods including LSTM in terms of prediction accuracy and
computational complexity. Being inspired of that, in this paper, an improved
TCN-based model, namely CNN-QoE, is proposed for continuously predicting the
QoE, which poses characteristics of sequential data. The proposed model
leverages the advantages of TCN to overcome the computational complexity
drawbacks of LSTM-based QoE models, while at the same time introducing the
improvements to its architecture to improve QoE prediction accuracy. Based on a
comprehensive evaluation, we demonstrate that the proposed CNN-QoE model can
reach the state-of-the-art performance on both personal computers and mobile
devices, outperforming the existing approaches.
