학술논문

Optimized resource distribution for interactive TV applications
Document Type
Periodical
Source
IEEE Transactions on Consumer Electronics IEEE Trans. Consumer Electron. Consumer Electronics, IEEE Transactions on. 61(3):344-352 Aug, 2015
Subject
Power, Energy and Industry Applications
Components, Circuits, Devices and Systems
Fields, Waves and Electromagnetics
Resource management
Quality of service
Media
Delays
Optimization
TV
Routing
Group video distribution
interactive television delivery
networking and computational resource optimization
social multimedia applications
Language
ISSN
0098-3063
1558-4127
Abstract
Though ubiquitous, the full potential of consumer electronic devices in the home, as content creators, remains underutilized due to the limited interaction between the consumers and the existing on-demand application and media services. Although services such as interactive television could change this, the geographic distribution of groups of consumers and the need for on-the-fly media processing that this entails, makes the efficient utilization of resources a complex optimization task requiring mechanisms to simultaneously allocate processing and network resources to groups of users. However, these technologies have not yet been developed, and brute force methods remain prohibitively complex. In order to overcome this problem, this paper proposes heuristic algorithms to both generate end-to-end delay bound multicast trees for individual groups of users and to co-locate multiple multicast trees, such that a minimum group quality metric can be satisfied. The performance of the proposed heuristic solution is evaluated in terms of the serving probability, i.e., the resource utilization efficiency, and computation time of the resource allocation decision making process. Simulation results show that improvements in the serving probability of up to 50%, in comparison with existing generic resource allocation schemes, and several orders of magnitude reduction of the computation time, in comparison to an optimal linear programming solution approach, can be achieved.