Fair Allocation of Utilities in Multirate Multicast Networks: A Framework for Unifying Diverse Fairness Objectives

We study fairness in a multicast network. We assume that different receivers of the same session can receive information at different rates. We study fair allocation of utilities, where utility of a bandwidth is an arbitrary function of the bandwidth. The utility function is not strictly increasing, nor continuous in general. We discuss fairness issues in this general context. Fair allocation of utilities can be modeled as a nonlinear optimization problem. However, nonlinear optimization techniques do not terminate in a finite number of iterations in general. We present an algorithm for computing a fair utility allocation. Using specific fairness properties, we show that this algorithm attains global convergence and yields a fair allocation in polynomial number of iterations

Back Pressure Based Multicast Scheduling for Fair Bandwidth Allocation

We study the fair allocation of bandwidth in multicast networks with multirate capabilities. In multirate transmission, each source encodes its signal in layers. The lowest layer contains the most important information and all receivers of a session should receive it. If a receiver’s data path has additional bandwidth, it receives higher layers which leads to a better quality of reception. The bandwidth allocation objective is to distribute the layers fairly. We present a computationally simple, decentralized scheduling policy that attains the maxmin fair rates without using any knowledge of traffic statistics and layer bandwidths. This policy learns the congestion level from the queue lengths at the nodes, and adapts the packet transmissions accordingly. When the network is congested, packets are dropped from the higher layers; therefore, the more important lower layers suffer negligible packet loss. We present analytical and simulation results that guarantee the maxmin fairness of the resulting rate allocation, and upper bound the packet loss rates for different layers

In-Network Congestion Control for Multirate Multicast

We present a novel control scheme that dynamically optimizes multirate multicast. By computing the differential backlog at every node, our scheme adaptively allocates transmission rates per session/user pair in order to maximize throughput. An important feature of the proposed scheme is that it does not require source cooperation or centralized calculations. This methodology leads to efficient and distributed algorithms that scale gracefully and can be embraced by low-cost wireless devices. Additionally, it is shown that maximization of sum utility is possible by the addition of a virtual queue at each destination node of the multicast groups. The virtual queue captures the desire of the individual user and helps in making the correct resource allocation to optimize total utility. Under the operation of the proposed schemes backlog sizes are deterministically bounded, which provides delay guarantees on delivered packets. To illustrate its practicality, we present a prototype implementation in the NITOS wireless testbed. The experimental results verify that the proposed schemes achieve maximum performance while maintaining low complexity.National Science Foundation (U.S.) (grant CNS-0915988)National Science Foundation (U.S.) (grant CNS-1116209)United States. Office of Naval Research (grant N00014-12-1-0064

Fair Bandwidth Allocation for Multicasting in Networks with Discrete Feasible Set

We study fairness in allocating bandwidth for loss-tolerant real-time multicast applications. We assume that the traffic is encoded in several layers so that the network can adapt to the available bandwidth and receiver processing capabilities by varying the number of layers delivered. We consider the case where receivers cannot subscribe to fractional layers. Therefore, the network can allocate only a discrete set of bandwidth to a receiver, whereas a continuous set of rates can be allocated when receivers can subscribe to fractional layers. Fairness issues differ vastly in these two different cases. Computation of lexicographic optimal rate allocation becomes NP-hard in this case, while lexicographic optimal rate allocation is polynomial complexity computable when fractional layers can be allocated. Furthermore, maxmin fair rate vector may not exist in this case. We introduce a new notion of fairness, maximal fairness. Even though maximal fairness is a weaker notion of fairness, it has many intuitively appealing fairness properties. For example, it coincides with lexicographic optimality and maxmin fairness, when maxmin fair rate allocation exists. We propose a polynomial complexity algorithm for computation of maximally fair rates allocated to various source-destination pairs, which incidentally computes the maxmin fair rate allocation, when the latter exists

Optimization Based Rate Control for Multicast with Network Coding

Recent advances in network coding have shown great potential for efficient information multicasting in communication networks, in terms of both network throughput and network management. In this paper, we address the problem of rate control at end-systems for network coding based multicast flows. We develop two adaptive rate control algorithms for the networks with given coding subgraphs and without given coding subgraphs, respectively. With random network coding, both algorithms can be implemented in a distributed manner, and work at transport layer to adjust source rates and at network layer to carry out network coding. We prove that the proposed algorithms converge to the globally optimal solutions for intrasession network coding. Some related issues are discussed, and numerical examples are provided to complement our theoretical analysis

Improving Multicast Communications Over Wireless Mesh Networks

In wireless mesh networks (WMNs) the traditional approach to shortest path tree based multicasting is to cater for the needs of the poorest performingnode i.e. the maximum permitted multicast line rate is limited to the lowest line rate used by the individual Child nodes on a branch. In general, this meansfixing the line rate to its minimum value and fixing the transmit power to its maximum permitted value. This simplistic approach of applying a single multicast rate for all nodes in the multicast group results in a sub-optimal trade-off between the mean network throughput and coverage area that does not allow for high bandwidth multimedia applications to be supported. By relaxing this constraint and allowing multiple line rates to be used, the mean network throughput can be improved. This thesis presents two methods that aim to increase the mean network throughput through the use of multiple line rates by the forwarding nodes. This is achieved by identifying the Child nodes responsible for reducing the multicast group rate. The first method identifies specific locations for the placement of relay nodes which allows for higher multicast branch line rates to be used. The second method uses a power control algorithm to tune the transmit power to allow for higher multicast branch line rates. The use of power control also helps to reduce the interference caused to neighbouring nodes.Through extensive computer simulation it can be shown that these two methods can lead to a four-fold gain in the mean network throughput undertypical WMN operating conditions compared with the single line rate case

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