论文标题
用户体验驱动的SSIM感知适应方法，用于DASH视频流
A User-experience Driven SSIM-Aware Adaptation Approach for DASH Video Streaming
论文作者
论文摘要
HTTP（DASH）上的动态自适应流是一种视频流技术。一个要点是位于客户方面的适应机制。这种机制极大地影响了视频流的整体体验质量（QOE）。在本文中，我们提出了一种用于破折号的新适应算法，即基于SSIM的适应性（SBA）。该机制是用户体验驱动的：它使用结构相似性索引测量（SSIM）作为主要视频感知质量指标；此外，适应性基于对SSIM指标和物理资源（缓冲区占用，带宽）的共同考虑，以最大程度地减少缓冲饥饿（重新封装）和视频质量不稳定，并最大程度地提高整体视频质量（通过SSIM）。为了评估我们的提案的绩效，我们用真实的交通轨迹进行了痕量驱动的仿真（在实际移动网络中捕获）。通过主要QOE指标与某些代表性算法（BBA，节日，OSMF）进行比较表明，我们的适应性算法SBA实现了有效的适应性，可以最大程度地降低重新恢复和不稳定性，而显示的视频则保持在较高的比特率中。
Dynamic Adaptive Streaming over HTTP (DASH) is a video streaming technique largely used. One key point is the adaptation mechanism which resides at the client's side. This mechanism impacts greatly on the overall Quality of Experience (QoE) of the video streaming. In this paper, we propose a new adaptation algorithm for DASH, namely SSIM Based Adaptation (SBA). This mechanism is user-experience driven: it uses the Structural Similarity Index Measurement (SSIM) as main video perceptual quality indicator; moreover, the adaptation is based on a joint consideration of SSIM indicator and the physical resources (buffer occupancy, bandwidth) in order to minimize the buffer starvation (rebuffering) and video quality instability, as well as to maximize the overall video quality (through SSIM). To evaluate the performance of our proposal, we carried out trace-driven emulation with real traffic traces (captured in real mobile network). Comparisons with some representative algorithms (BBA, FESTIVE, OSMF) through major QoE metrics show that our adaptation algorithm SBA achieves an efficient adaptation minimizing both the rebuffering and instability, whereas the displayed video is maintained at a high level of bitrate.
