一种动态优化多用户无线视频传输的系统框架
A Systematic Framework for Dynamically Optimizing Multi-User Wireless Video Transmission
摘要：
在本文中，我们制定的协作多用户无线视频传输问题作为一个多用户马尔可夫决策过程（MUMDP）明确考虑用户的异构视频业务特性，时变网络条件和无线用户之间的动态耦合。在现有的多用户视频传输解决方案中，这些环境动态通常被忽略。为了符合无线网络的分散性，我们建议使用拉格朗日松弛将MUMDP分解为本地MDP。不同于传统的多用户视频传输解决方案源于网络效用最大化框架，所提出的分解使每个无线用户能够单独解决自己的动态跨层优化（即本地MDP）和网络协调器更新拉格朗日乘数不仅基于所有用户的当前资源需求，而且基于所有用户的未来资源需求来确定资源价格（即，资源价格），使得所有用户的长期视频质量被最大化。然而，解决MUMDP需要经验的环境动态的统计知识，这往往是不可用的传输时间之前。为了克服这一障碍，我们提出了一种新的在线学习算法，它允许无线用户在一个时隙内的多个状态更新他们的策略。这与传统的学习解决方案不同，传统的学习解决方案通常每个时隙更新一个状态。所提出的学习算法可以显著提高学习性能，从而随着时间的推移显著提高无线用户体验的视频质量。我们的仿真结果表明，所提出的MUDDP框架的效率相比，传统的多用户视频传输解决方案。
英文摘要：
In this paper, we formulate the collaborative multi-user wireless video transmission problem as a multi-user Markov decision process (MUMDP) by explicitly considering the users' heterogeneous video traffic characteristics, time-varying network conditions and the resulting dynamic coupling between the wireless users. These environment dynamics are often ignored in existing multi-user video transmission solutions. To comply with the decentralized nature of wireless networks, we propose to decompose the MUMDP into local MDPs using Lagrangian relaxation. Unlike in conventional multi-user video transmission solutions stemming from the network utility maximization framework, the proposed decomposition enables each wireless user to individually solve its own dynamic cross-layer optimization (i.e. the local MDP) and the network coordinator to update the Lagrangian multipliers (i.e. resource prices) based on not only current, but also future resource needs of all users, such that the long-term video quality of all users is maximized. However, solving the MUMDP requires statistical knowledge of the experienced environment dynamics, which is often unavailable before transmission time. To overcome this obstacle, we then propose a novel online learning algorithm, which allows the wireless users to update their policies in multiple states during one time slot. This is different from conventional learning solutions, which often update one state per time slot. The proposed learning algorithm can significantly improve the learning performance, thereby dramatically improving the video quality experienced by the wireless users over time. Our simulation results demonstrate the efficiency of the proposed MUMDP framework as compared to conventional multi-user video transmission solutions.
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