Rate adaptation with Bayesian attractor model for MPEG-DASH
Rate adaptation with Bayesian attractor model for MPEG-DASH
复制标题
MPEG-DASH 的贝叶斯吸引子模型的速率自适应
DOI：
10.1109/ccwc.2019.8666543
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
M. Murata
中科院分区：
文献类型：
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作者：
Masayoshi Iwamoto;Tatsuya Otoshi;D. Kominami;M. Murata
近年来，Over-the-top视频服务提供商在提供视频内容时，将体验质量(QOE)作为重要因素。今天，大多数视频流媒体服务提供商，如YouTube和Netflix，通过自适应比特率(ABR)控制技术向用户提供视频内容，以提高用户的QOE。为了在波动的网络条件下最大化QOE，本文提出了一种基于贝叶斯吸引子模型的ABR算法，顾名思义，它根据贝叶斯推理对人脑的认知和决策进行建模。仿真结果表明，即使在网络可用带宽波动较大的情况下，我们提出的方法也能以较少的视频质量切换实现更高的视频比特率，从而改善用户的QOE。
Recently, over-the-top video service providers focus on the quality of experience (QoE) as an important factor when they provide video content. Today, most video streaming service providers, such as Youtube and Netflix, provide video content to users with adaptive bitrate (ABR) control techniques for increasing the user QoE. To maximize the QoE under a fluctuating network condition, in this paper, we propose an ABR algorithm using the Bayesian attractor model, which models cognition and decision making of the human brain, as the name suggests, according to the Bayesian inference. Simulation results show that our proposed method achieves a higher video bitrate with less video quality switching to improve the user QoE compared to the methods even in the situation where network available bandwidth greatly fluctuates.