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The outcome of this is a Pareto front of optimal strategies, all of which would satisfy the objectives. Conflicting goals are introduced, in the form of a population of nodes actively seeking to acquire neighbours of a type different from their own, resulting in decreased local homogeneity. Finally, we introduce dynamic goals by making the preferred neighbour type a function of the local characteristics of a simulated workload. Scalability issues with respect to the key parameters of system size and diversity are extensively discussed. 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. This article refines 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. We measure the ability of a selected set of local rules to foster self-organization of what is originally a random