CSE Technical Seminar Report on Multicast Rate Control PPT

Introduction to Technical Seminar Topic on Multicast Rate Control:

This project deal with control of data flow in multiple rates over the layered network and rate control difficulty for multicast interchange. The existing twofold based approach, the algorithm existing scales well as the more multicast groups in the system is increases. Unlike all offered approach, our approach takes into account the discreteness of the recipient rates that is inherent to layered multicasting.

In this analytically with the aim of our algorithm come together and yields rates that are approximately optimal. Simulation passed out in an asynchronous set of connections situation demonstrates that our algorithm exhibits high-quality union speed and least speed fluctuations. Multi rate or multi layer transition is the more chosen structure of data transfer when receivers of the same multicast group have dissimilar characteristics. In this multi rate system, signals are transferred over the network and these signals are encoded into different number of layers. Layers are become combined to provide progressive improvement. To increase network use sage efficiently, an effective rate control strategy is required. Multicast is data transfer strategy that transfer data from a single sender to two or more receivers.

This data transfer technique has more number of advantages. They are, all receivers get the data in same data rate, sender may adapts with the low speed receiver, a solo slow recipient can pull down the data rate for the whole group and have less bandwidth consumption. The problem with the existing system is service maximization based rate control problem can be formulate. This occurred when a cast rate changes with each associated receiver and formulated the optimization problem in terms of these receiver cast rate variables.

        The proposed system for multi rate which help to develop an iterative algorithm that achieves rates that are provably close-to-optimal along with dynamic programming, it leads to an algorithm that is completely distributed in nature. For common numeral programs, lagrangian entertainment may not direct to closest- optimal solutions and the algorithm may not be distributed.

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