Tree multicast strategies in mobile, multishop wireless networks
Mobile Networks and Applications
OMNeT: Objective Modular Network Testbed
MASCOTS '93 Proceedings of the International Workshop on Modeling, Analysis, and Simulation On Computer and Telecommunication Systems
Adaptive demand-driven multicast routing in multi-hop wireless ad hoc networks
Adaptive demand-driven multicast routing in multi-hop wireless ad hoc networks
The Lagrangian Relaxation Method for Solving Integer Programming Problems
Management Science
The multimedia broadcast-multicast service
Wireless Communications & Mobile Computing
Efficient Resource Allocation for Wireless Multicast
IEEE Transactions on Mobile Computing
An architecture for adaptive multimedia streaming to mobile nodes
Proceedings of the 6th International Conference on Advances in Mobile Computing and Multimedia
A cooperative multicast scheduling scheme for multimedia services in IEEE 802.16 networks
IEEE Transactions on Wireless Communications
Mobile multicast mechanism based MIH for efficient network resource usage in heterogeneous networks
ICACT'10 Proceedings of the 12th international conference on Advanced communication technology
A Cross-Layer Optimization Framework for Multihop Multicast in Wireless Mesh Networks
IEEE Journal on Selected Areas in Communications
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Coexistence of various wireless access networks and the ability of mobile terminals to switch between them make an optimal selection of serving networks for multicast groups a challenging problem. Since optimal network selection requires large dimensions of data to be collected from several network locations and sent between several network components, the scalability can easily become a bottleneck in large-scale systems. Therefore, reducing data exchange within heterogeneous wireless networks is important. We study the decision-making process and the data that needs to be sent between different network components. We present two decentralized solutions to this problem that operate with reduced sets of information. We define the upper and lower bounds to these solutions and evaluate them in the OMNet++ simulation environment. Both solutions provide a substantial improvement in performance compared to the lower bound.