Consensus algorithms provide a framework for the distributed coordination of a multi-agent system. However, widespread application and deployment of consensus algorithms may be limited in real-world multi-agent coordination problems due to implementation on size, power, and weight constrained vehicles. In this case, limited resources may contribute to delay and packet loss causing algorithm deterioration and violation of performance guarantees. This calls for novel strategies for intelligent resource utilization and computationally simple implementation. Towards this goal, we propose co-regulation strategies for discrete time average consensus under delays allowing dynamic resource utilization while coping with communication limitations. This is done by dynamically adjusting communication frequency to facilitate higher state exchange rates while simultaneously adjusting agents' locations to increase inter-agent connectivity for rapid convergence. We prove that convergence is still guaranteed for co-regulation strategies for discrete time average consensus under bounded delays. In addition, we propose a pause for agents' locations to mitigate adverse behavior caused by delay. To simplify implementation we devise a consensus strategy that decouples the co-regulated consensus from low-level vehicle feedback control. The usability of our proposed system is evaluated through a series of simulations, and we show our proposed co-regulation strategies in fact result in faster convergence time. We evaluate the approach with an outdoor experiment using 4 customized unmanned aircraft systems (UASs).