Network coding: an excellent approach for overloaded communication era
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The transmission of information within a data network is constrained by the network topology and link capacities. In this thesis, we study the fundamental upper bound of information transmission rates with these constraints, given the unique replicable and encodable properties of information flows. We first analyze the theoretical bounds on throughput improvement due to coding, in various network scenarios. Then based on recent advances in multicast rates with network coding, we formulate the maximum multicast rate problem as a linear network optimization problem, assuming the general undirected network model. We design an efficient primal subgradient solution to this problem, based on Lagrangian relaxation techniques. We then extend our discussions to one or multiple communication sessions, each in the form of unicast, broadcast, multicast, or group communication. We also consider overlay and ad hoc network models. We show that although network coding does not dramatically increase the absolute achievable throughput, it facilitates the design of efficient algorithms to achieve such optimal throughput.