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.

中文翻译：

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用于视频流服务中连续 QoE 预测的卷积神经网络

在视频流服务中，预测连续用户的体验质量 (QoE) 在向用户提供高质量流内容方面起着至关重要的作用。然而，由 QoE 数据中的时间依赖性和 QoE 影响因素之间的非线性关系引起的复杂性给连续 QoE 预测带来了挑战。为了解决这个问题，现有研究利用长短期记忆模型 (LSTM) 来有效地捕获这种复杂的依赖关系，从而获得出色的 QoE 预测精度。然而，LSTM 的高计算复杂度，由其架构中的顺序处理特性引起，引发了关于其在计算能力有限的设备上的性能的严重问题。同时，时间卷积网络（TCN），卷积神经网络的一种变体，最近被提出用于序列建模任务（例如，语音增强），在预测精度和计算复杂性方面提供优于包括 LSTM 在内的基线方法的预测性能。受此启发，本文提出了一种改进的基于 TCN 的模型，即 CNN-QoE，用于连续预测 QoE，该模型具有序列数据的特征。所提出的模型利用 TCN 的优势来克服基于 LSTM 的 QoE 模型的计算复杂性缺点，同时对其架构进行改进以提高 QoE 预测精度。根据综合评价，