基于DASH的多媒体流qos增强自适应算法

Dongeun Suh, Insun Jang, Sangheon Pack
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引用次数: 29

摘要

在基于HTTP的动态自适应流(DASH)中,客户端连续估计可用的网络带宽并决定即将下载的视频块的传输速率。尽管文献中已经报道了DASH的一些增强功能,但它们并没有很好地研究当前的缓冲状态,以应对波动的网络条件,因此无法实现无缝视频流。在本文中,我们提出了一种新的速率自适应算法,称为体验质量(QoE)增强自适应算法(QAAD),它保留了最小的缓冲长度以避免中断,并最大限度地减少了播放过程中的视频质量变化。我们实施了DASH测试平台并进行了大量实验。实验结果表明,在波动的网络条件下,QAAD提供了稳定的视频质量的无缝流,而之前的缓存感知算法(即qos感知的DASH)经常改变视频质量并经历中断。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
QoE-enhanced adaptation algorithm over DASH for multimedia streaming
In dynamic adaptive streaming over HTTP (DASH), a client consecutively estimates the available network bandwidth and decides the transmission rate for the forthcoming video chunks to be downloaded. Even though several enhancements to DASH have been reported in the literature, they do not well-investigate the current buffer status to cope with fluctuating network conditions and thus do not achieve seamless video streaming. In this paper, we propose a novel rate adaptation algorithm called quality of experience (QoE)-enhanced adaptation algorithm over DASH (QAAD), which preserves the minimum buffer length to avoid interruption and minimizes the video quality changes during the playback. We implemented a DASH testbed and conducted extensive experiments. Experimental results demonstrate that under fluctuating network conditions, QAAD provides seamless streaming with stabilized video quality while the previous buffer-aware algorithm (i.e., QoE-aware DASH) frequently changes the video quality and undergoes the interruption.
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