In today's Internet, HTTP Adaptive Streaming (HAS) is the mainstream standard for video streaming, which switches the bitrate of the video content based on an Adaptive BitRate (ABR) algorithm. An effective Quality of Experience (QoE) assessment metric can provide crucial feedback to an ABR algorithm. However, predicting such real-time QoE on the client side is challenging. The QoE prediction requires high consistency with the Human Visual System (HVS), low latency, and blind assessment, which are difficult to realize together. To address this challenge, we analyzed various characteristics of HAS systems and propose a non-uniform sampling metric to reduce time complexity. Furthermore, we design an effective QoE metric that integrates resolution and rebuffering time as the Quality of Service (QoS), as well as spatiotemporal output from a deep neural network and specific switching events as content information. These reward and penalty features are regressed into quality scores with a Support Vector Regression (SVR) model. Experimental results show that the accuracy of our metric outperforms the mainstream blind QoE metrics by 0.3, and its computing time is only 60\% of the video playback, indicating that the proposed metric is capable of providing real-time guidance to ABR algorithms and improving the overall performance of HAS.

翻译：在当今互联网中，HTTP自适应流（HAS）已成为视频流的主流标准，其通过自适应码率（ABR）算法切换视频内容的码率。有效的体验质量（QoE）评估指标可为ABR算法提供关键反馈。然而，在客户端实时预测此类QoE具有挑战性。QoE预测需要与人类视觉系统（HVS）保持高度一致性、低延迟以及盲评估，这些要求难以同时实现。为应对这一挑战，我们分析了HAS系统的多种特性，提出一种非均匀采样指标以降低时间复杂度。进一步，我们设计了一种有效的QoE指标，将分辨率与缓冲时间作为服务质量（QoS）指标，同时整合深度神经网络的时空输出与特定切换事件作为内容信息。这些奖励与惩罚特征通过支持向量回归（SVR）模型回归为质量评分。实验结果表明，本指标在准确性上超出主流盲QoE指标0.3，且计算时间仅为视频播放时长的60%，表明所提指标能够为ABR算法提供实时指导，并提升HAS的整体性能。