Layered Media Multicast Control (LMMC): Real-Time Error Control

We study the problem of real-time error control in layered and replicated media systems. We formulate an optimization problem aimed at minimizing a cost metric defined over the wasted bandwidth of redundancy in such systems. We also provide an analytical solution to the problem in the context of layered media multicast control (LMMC) protocol. In doing so, we present closed-form expressions describing the temporally correlated loss pattern of communication networks. Utilizing our closed-form expressions, we rely on an a priori estimate of loss along with a hybrid proactive FEC-ARQ scheme to statistically guarantee the QoS for the receivers of a media system. We show the effectiveness of our protocol by means of simulating realistic error control scenarios     

A neural-based technique for estimating self-similar trafficaverage queueing delay

Estimating buffer latency is one of the most important challenges in the analysis and design of traffic control algorithms. In this paper a novel approach for estimating average queueing delay in multiple source queueing systems is introduced. The approach relies on the modeling power of neural networks in predicting self-similar traffic patterns in order to determine the arrival rate and the packet latency of low loss, moderately loaded queueing systems accommodating such traffic patterns     

Layered media multicast control (LMMC): rate allocation and partitioning

The objective of layering techniques of distributing multimedia traffic over multicast IP networks is to effectively cope with the challenges in continuous media applications. The challenges include heterogeneity, fairness, real-time constraints, and quality of service. We study the problem of rate allocation and receiver partitioning in layered and replicated media systems. We formulate an optimization problem aimed at maximizing a close approximation of the so-called max-min fairness metric subject to loss and bandwidth constraints. Our optimal layered media multicast control (LMMC) solution to the problem analytically determines the layer rates and the corresponding partitioning of the receivers. Our simulation results show the effectiveness of our proposed solution in realistic scenarios.     

Dynamic neural-based buffer management for queuing systems with self-similar characteristics

Buffer management in queuing systems plays an important role in addressing the tradeoff between efficiency measured in terms of overall packet loss and fairness measured in terms of individual source packet loss. Complete partitioning (CP) of a buffer with the best fairness characteristic and complete sharing (CS) of a buffer with the best efficiency characteristic are at the opposite ends of the spectrum of buffer management techniques. Dynamic partitioning buffer management techniques aim at addressing the tradeoff between efficiency and fairness. Ease of implementation is the key issue when determining the practicality of a dynamic buffer management technique. In this paper, two novel dynamic buffer management techniques for queuing systems accommodating self-similar traffic patterns are introduced. The techniques take advantage of the adaptive learning power of perceptron neural networks when applied to arriving traffic patterns of queuing systems. Relying on the water-filling approach, our proposed techniques are capable of coping with the tradeoff between packet loss and fairness issues. Computer simulations reveal that both of the proposed techniques enjoy great efficiency and fairness characteristics as well as ease of implementation.