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:在当今的互联网中,HTTP自适应流(HAS)是视频流媒体的主流标准,它基于自适应比特率(ABR)算法切换视频内容的比特率。有效的体验质量度量可以为ABR算法提供关键反馈。然而,预测客户端的实时QoE是具有挑战性的。QoE预测需要与人类视觉系统(HVS)高度一致,低延迟和盲评估,这些要素很难同时实现。为解决这一挑战,我们分析了HAS系统的各种特征并提出了一种非均匀采样度量,以降低时间复杂度。此外,我们设计了一种有效的QoE度量,将分辨率和重新缓冲时间作为服务质量(QoS),以及来自深度神经网络的时空输出和特定切换事件作为内容信息。这些奖励和惩罚特征与支持向量回归(SVR)模型相结合,回归为质量分数。实验结果表明,我们的度量精度优于主流盲QoE度量0.3,并且其计算时间仅为视频播放的60%,表明所提出的度量能够为ABR算法提供实时指导,并提高HAS的整体性能。