Improving quality of experience in adaptive low latency live streaming
Improving quality of experience in adaptive low latency live streaming
复制标题
提高自适应低延迟直播的体验质量
DOI：
10.1007/s11042-023-15895-9
复制
发表时间：
2023
影响因子：
3.6
通讯作者：
Lyko T
中科院分区：
文献类型：
--
作者：
Lyko T
HTTP自适应流媒体（HAS）是互联网上最重要的流媒体视频技术，与传统广播方法相比，它存在端到端延迟较高的问题。这种延迟是由于内容作为片段而不是作为连续流进行交付而导致的，这要求客户端缓冲大量数据以提供对网络吞吐量变化的弹性，并实现内容的连续播放而不会停顿。客户端使用自适应比特率（ABR）算法来选择请求每个片段的质量，以在避免停顿的情况下权衡视频质量，从而提高体验质量（QoE）。ABR算法响应网络条件变化的速度会影响需要缓冲的数据量，因此为了实现低延迟，ABR需要快速响应。Llama（Lyko等人）是一种新的低延迟ABR算法，我们以前提出并评估了四种按需ABR算法。在本文中，我们报告了对Llama的评估，该评估展示了其对低延迟流的适用性，并在多个QoE指标和各种网络场景中将其性能与三种最先进的低延迟ABR算法进行了比较。此外，我们报告了一个广泛的主观测试，以评估视频质量的变化对QoE的影响，其中的变化是来自ABR行为的评估中观察到的，使用短片段和场景。我们将完整发布主观测试结果，并向研究社区提供我们的吞吐量跟踪。
HTTP Adaptive Streaming (HAS), the most prominent technology for streaming video over the Internet, suffers from high end-to-end latency when compared to conventional broadcast methods. This latency is caused by the content being delivered as segments rather than as a continuous stream, requiring the client to buffer significant amounts of data to provide resilience to variations in network throughput and enable continuous playout of content without stalling. The client uses an Adaptive Bitrate (ABR) algorithm to select the quality at which to request each segment to trade-off video quality with the avoidance of stalling to improve the Quality of Experience (QoE). The speed at which the ABR algorithm responds to changes in network conditions influences the amount of data that needs to be buffered, and hence to achieve low latency the ABR needs to respond quickly. Llama (Lyko et al. ) is a new low latency ABR algorithm that we have previously proposed and assessed against four on-demand ABR algorithms. In this article, we report an evaluation of Llama that demonstrates its suitability for low latency streaming and compares its performance against three state-of-the-art low latency ABR algorithms across multiple QoE metrics and in various network scenarios. Additionally, we report an extensive subjective test to assess the impact of variations in video quality on QoE, where the variations are derived from ABR behaviour observed in the evaluation, using short segments and scenarios. We publish our subjective testing results in full and make our throughput traces available to the research community.
CMAF在直播场景下的评估
DOI：
--
发表时间：
2020
期刊：
International Workshop on Network and Operating System Support for Digital Audio and Video
影响因子：
--
作者：
Tomasz Lyko;M. Broadbent;N. Race;M. Nilsson;Paul Farrow;S. Appleby
通讯作者：
S. Appleby
Stallion：低延迟视频流的视频自适应算法
DOI：
10.1145/3339825.3397044
发表时间：
2020
期刊：
2020
影响因子：
--
作者：
Gutterman, Craig;Fridman, Brayn;Gilliland, Trey;Hu, Yusheng;Zussman, Gil
通讯作者：
Zussman, Gil
基于 HTTP 的自适应流应用程序的模拟框架
DOI：
--
发表时间：
2017
期刊：
Workshop on ns-3
影响因子：
--
作者：
Harald Ott;K. Miller;A. Wolisz
通讯作者：
A. Wolisz