A Meta-Learning Framework for Learning Multi-User Preferences in QoE Optimization of DASH
A Meta-Learning Framework for Learning Multi-User Preferences in QoE Optimization of DASH
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DOI：
10.1109/tcsvt.2019.2939282
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发表时间：
2020-09
影响因子：
8.4
通讯作者：
Liangyu Huo;Zulin Wang;Mai Xu;Yong Li;Z. Ding;H. Wang
中科院分区：
文献类型：
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作者：
Liangyu Huo;Zulin Wang;Mai Xu;Yong Li;Z. Ding;H. Wang
基于超文本传输的动态自适应视频流协议(DASH)在互联网视频传输中起着关键作用。传统的DASH适配方法主要着眼于优化所有客户端的整体体验质量，而忽略了不同用户的体验质量差异性。本文提出了一种多用户偏好元学习框架(MLMP)，作为一种新的DASH自适应方法，能够优化不同用户的不同QOE。具体地说，我们首先设计了一个主观实验来分析用户之间的QOE偏好差异，其中QOE指的是视觉质量、波动和重缓冲事件的度量。在此基础上，我们将多用户偏好的QOE优化问题描述为一个多任务深度强化学习问题。在我们的公式中，通过将权重分配给三个QOE度量，在整体QOE计算中对每个用户的QOE偏好进行建模。然后，开发了MLMP框架来解决所提出的多任务DRL问题，使得在DASH自适应中，关于视觉质量、波动和重缓冲事件的偏好可以针对不同的用户进行优化。仿真结果表明，该方法在满足不同用户对视觉质量、波动和重缓冲事件的QOE偏好方面优于现有的DASH自适应方法。
Dynamic adaptive video streaming over hypertext transfer protocol (DASH) plays a key role in video transmission over the Internet. The conventional DASH adaptation approaches mainly focus on optimizing the overall quality of experience (QoE) for all client sides, neglecting the QoE diversity of different users. In this paper, we propose a meta-learning framework for multi-user preferences (MLMP) as a new DASH adaptation approach, which is able to optimize the diverse QoE of different users. Specifically, we first design a subjective experiment to analyze the difference of QoE preferences across users, in which QoE refers to the metrics of visual quality, fluctuation, and rebuffering events. Based on our findings, we formulate the QoE optimization of multi-user preferences as a multi-task deep reinforcement learning (DRL) problem. In our formulation, the QoE preference of each user is modeled in the overall QoE calculation via assigning the weights to the three QoE metrics. Then, the MLMP framework is developed to solve the proposed multi-task DRL problem, such that the preferences regarding visual quality, fluctuation, and rebuffering events can be optimized for different users in DASH adaptation. Finally, the simulation results show that the proposed approach outperforms state-of-the-art DASH adaptation approaches in satisfying the different users’ QoE preferences regarding visual quality, fluctuation, and rebuffering events.