Aside from the introduction of many new schemes, the use of TCP-based AQM schemes and in specific RED is anticipated to continue in foreseeable future as the de-facto standard of network congestion control. Therefore, conducting extra research work aiming at improving the performance of RED is still a topic of high interest. In this paper, we present an analytical study aiming at the fine tuning of the RED parameters. Utilizing a statistical analysis approach, we formulate an optimization problem aimed at addressing the loss and delay tradeoff of the RED queuing discipline. We provide a two-phase iterative solution to the problem in order to identify the settings of the RED parameters. We discuss the convergence characteristics of our solution and investigate its low complexity characteristics. Through extensive NS2 experiments, we illustrate the advantages of our proposed optimization approach by comparing its results to those of adaptive RED as well as standard RED with recommended parameter settings.

Achieving minimal loss while satisfying an acceptable delay profile remains to be an open problem under the RED queuing discipline. In this paper, we present a framework targeted at optimal fine tuning of the RED parameters in order to address such problem. For a given traffic pattern and utilizing a statistical analysis of finite-state Markov chains, we formulate an optimization problem aimed at addressing the loss and delay tradeoff of the RED queuing discipline. Our two-step iterative solution to the problem identifies the optimal settings of the RED parameters. We prove the convergence of our solution and investigate its low complexity characteristics. We apply our framework to a number of generic queuing and TCP scenarios in order to capture loss and delay performance of our algorithms versus buffer capacity and service rate. Based on our results, we argue that our model is capable of optimally addressing the loss-delay tradeoff of RED queues accommodating time-varying traffic profiles.

We describe a cross-layer study that attempts at profiling the performance of end-to-end congestion control protocols in high BDP wireless networks. Packets transmitted over such networks are assumed to face temporally correlated random bit errors caused by fading and/or blocking. We utilize a finite-state Markov chain to model bit error characteristics and apply per packet link layer FEC codes in order to compensate for such errors. Utilizing our model along with loss expressions, we profile the end-to-end performance of XCP, VCP, and TCP/AQM+ECN congestion control protocols. Based on our profiling results, we observe that (1) the performance of any congestion control protocol highly degrades without properly protecting its data and signaling information against random bit errors, (2) utilizing multiple antenna nodes improves the transient characteristics of the congestion control protocol in terms of utilization and packet error rates, and (3) XCP and VCP represent the best alternatives of congestion control for high bandwidth moderate delay and moderate bandwidth large delay environments, respectively.

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

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.