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Genetic Algorithms may be used together with Pareto Optimality in the process of selection of a suitable video content adaptation strategy, the former to return best or fittest solutions that have evolved over many generations and the latter to evaluate and rank each generation's solutions against a set of objectives without the need to assign weights to each one. The outcome of this is a Pareto front of optimal strategies, all of which would satisfy the objectives. In order to make the refined process dynamic, it pursues its implementation using Self-Organising Neural Networks. Professor Kamareddine is an Associate Editor of The Computer Journal and is based in the Department of Mathematical and Computer Sciences at Heriot-Watt University, Edinburgh, UK. Finally, we introduce dynamic goals by making the preferred neighbour type a function of the local characteristics of a simulated workload. The distribution of optimal strategies on a Pareto front, however, suggests that there may be a ‘best-fit’ optimal strategy. We demonstrate that in this context, an overlay rewiring process based purely on local decisions and interactions can result in efficient load-balancing without central planning. We measure the ability of a selected set of local rules to foster self-organization of what is originally a random graph into a structured network..ArrayThe Capsule Reviews are intended to provide a short succinct review of each paper in the issue in order to bring to a wider readership. We conclude by discussing the implications of our findings for the design of future distributed applications, the likely influence of other factors and of extreme parameter values on the ability of the system to self-organize and the potential improvements to our framework. This article refines the process of selection of an optimal strategy by taking into account this distribution alongside user preferences, video content characteristics and usage history. The