QoE Assessment Model Based on Continuous Deep Learning for Video in Wireless Networks

Quality of experience (QoE) is a vital metric that indicates how well the wireless network provides transmission services to users, while quality of service (QoS) help better configure the network parameters for higher performance. The evaluation time of QoE is usually several orders of magnitude larger than that of QoS, because QoE is the perception of users over a period of time, but QoS can be collected every millisecond. Therefore, the implementation of QoE/QoS mapping model can help us obtain QoE by collecting the QoS measurements, and perform QoE-based network configurations with smaller time granularity. Many studies are made to obtain the QoS to QoE mapping, including the use of machine learning (ML) methods. However, traditional ML-based regression methods for QoE/QoS mapping face the challenge of high regression error and catastrophic forgetting in dealing with continuously arriving data. In this paper, we propose a novel QoE model based on continual deep learning in wireless network. This model is formed with two deep neural networks (DNNs) concatenated. The first DNN classifies data into different subsets, which are then fed into the second DNN for regression. The second DNN dynamically form the corresponding subnets, each with nodes and connections adaptively selected in each new time period with new arriving data. We solve the catastrophic forgetting problem with the use of node splitting and hidden state augmentation. Our proposed learning framework greatly reduces the regression error to as low as 0.9314%. The experimental results demonstrate that our proposed model reduces the root mean square error (RMSE) by $21 \sim 86$ times compared with several existing approaches, specially, the testing error of our proposed model is more than 80 times lower than that of traditional DNN. Compared with other DNN-based cascade models, our proposed method provides good performance in both training time and RMSE.