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Dynamic resource allocation for multimedia communications over distributed systems

分布式系统上多媒体通信的动态资源分配

基本信息

批准号：
386107-2010
负责人：
He, Yifeng
金额：
$ 1.68万
依托单位：
Ryerson University
依托单位国家：
加拿大
项目类别：
Discovery Grants Program - Individual
财政年份：
2010
资助国家：
加拿大
起止时间：
2010-01-01 至 2011-12-31
项目状态：
已结题

来源：
https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=463658
关键词：
Dynamic resource allocation multimedia communications

项目摘要

The advances of Internet and wireless access technologies have opened up new opportunities to develop high-quality multimedia applications. Many multimedia applications involve real-time multimedia communications over distributed systems, in which there is no centralized controller. Examples of the distributed systems are Peer-to-Peer (P2P) networks and multi-hop wireless networks. A distributed system is typically dynamic, since the source model, the channel condition, and the overlay topology are time-varying. It is a great challenge to optimize the resource allocation for multimedia communications over a dynamic distributed system. In this project, a new framework for dynamic resource allocation is proposed to address this challenge. Under the proposed framework, each node proactively collects the information about the system, adaptively learns the system models, and then optimizes the actions in a collaborative and distributed manner. The proposed framework will be applied to solve the following six problems: 1) information collection and adaptive learning in a distributed system, 2) achievable video quality in P2P streaming systems, 3) cross-channel resource optimization for multi-channel P2P streaming systems, 4) cross-layer optimization for video streaming over multi-hop cognitive radio networks, 5) video quality maximization for wireless visual sensor networks under power constraints, and 6) context-aware health monitoring by integrating body sensor networks and wireless visual sensor networks.

互联网和无线接入技术的发展为开发高质量的多媒体应用提供了新的机会。许多多媒体应用涉及分布式系统上的实时多媒体通信，其中没有集中控制器。分布式系统的示例是对等（P2P）网络和多跳无线网络。分布式系统通常是动态的，因为源模型、信道条件和覆盖拓扑是时变的。在动态分布式系统中，如何优化多媒体通信的资源分配是一个巨大的挑战。在这个项目中，提出了一个新的动态资源分配框架，以应对这一挑战。在该框架下，每个节点主动收集系统的信息，自适应地学习系统模型，然后以协作和分布式的方式优化动作。拟议的框架将用于解决以下六个问题：1）分布式系统中的信息收集和自适应学习，2）P2P流传输系统中可实现的视频质量，3）用于多信道P2P流传输系统的跨信道资源优化，4）用于多跳认知无线电网络上的视频流传输的跨层优化，5）在功率约束下无线视觉传感器网络的视频质量最大化，以及6）通过集成身体传感器网络和无线视觉传感器网络的上下文感知健康监测。