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 provide a high QoE prediction performance on both personal computers and mobile devices, outperforming the existing approaches.INDEX TERMS Convolutional neural networks, temporal convolutional network, quality of experience, video streaming.

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HTTP/2 video streaming has gotten a lot of attention in the development of multimedia technologies over the last few years. In HTTP/2, the server push mechanism allows the server to deliver more video segments to the client within a single request in order to deal with the requests explosion problem. As a result, recent research efforts have been focusing on utilizing such a feature to enhance the streaming experience while reducing the request-related overhead. However, current works only optimize the performance of a single client, without necessary concerns of possible influences on other clients in the same network. When multiple streaming clients compete for a shared bandwidth in HTTP/1.1, they are likely to suffer from unfairness, which is defined as the inequality in their bitrate selections. For HTTP/1.1, existing works have proven that the network-assisted solutions are effective in solving the unfairness problem. However, the feasibility of utilizing such an approach for the HTTP/2 server push has not been investigated. Therefore, in this paper, a novel proxy-based framework is proposed to overcome the unfairness problem in adaptive streaming over HTTP/2 with the server push. Experimental results confirm the outperformance of the proposed framework in ensuring the fairness, assisting the clients with avoiding rebuffering events and lowering bitrate degradation amplitude, while maintaining the mechanism of the server push feature.

HTTP Adaptive Streaming (HAS) has recently become the de facto choice of today's streaming providers to perform a smooth video content delivery to the end users. The key technology behind HAS is the adaptive bitrate selection (ABR) algorithm that adaptively selects the best suitable video bitrate based on either throughput or buffer monitoring techniques. In order to fulfill user's satisfaction, ABRs must be designed to accurately reflect the perceived quality of experience (QoE), which is influenced by the perceptual and technical factors. However, both throughput and buffer only account for the technical factors, leading to the insufficiency of today's ABRs in demonstrating human perception. Moreover, existing throughput and buffer-based algorithms are slow-responsive to significant network changes and unstable in terms of video quality, as found by recent research efforts. For those reasons, QABRa novel QoE-based bitrate selection algorithmis proposed in this paper that combines the underlying network parameters and user's instantaneous QoE (in accordance with perceptual factors). Experimental results demonstrate that QABR outperforms the referenced baseline algorithm in various evaluation criteria.

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