Synchronous dataflow graphs (SDFGs) are widely used to model streaming applications such as signal processing and multimedia applications in embedded systems. Trade-off analysis between performance and resource usage of SDFGs allows designers to explore implementation alternatives of a system while meeting its performance requirements and resource constraints. This type of analysis is computationally very challenging, particularly when resources may be shared among computations. With resource sharing, system scheduling decisions lead to a combinatorial explosion in the number of scheduling alternatives to be explored. We present a new approach to explore the trade-offs in a such systems. It breaks analysis down in iterations of dataflow graph execution and uses a max-plus algebra semantics. The experimental results on a set of realistic benchmark models show that the new iteration-based approach and the traditional time-based analysis approach complement each other. None of the two approaches dominates the other in terms of quality of the analysis results and analysis time. The two approaches combined give the highest quality result.