Process Networks are a means to describe streaming embedded applications. They rely on explicit representation of task concurrency, pipeline and data-flow. Originally, Data-Flow Process Network (DFPN) representations are independent from any execution platform support model. Such independence is actually what allows looking next for adequate mappings. Mapping deals with scheduling and distribution of computation tasks onto processing resources, but also distribution of communications to interconnects and memory resources. This design approach requires a level of description of execution platforms that is both accurate and simple. Recent platforms are composed of repeated elements with global interconnection (GPU, MPPA). A parametric description could help achieving both requirements. Then, we argue that a model-driven engineering approach may allow to unfold and expand an original DFPN model, in our case a so-called Synchronous DataFlow graph (SDF) into a model such that: a) the original description is a quotient refolding of the expanded one, and b) the mapping to a platform model is a grouping of tasks according to their resource allocation. Then, given such unfolding, we consider how to express the allocation and the real-time constraints. We do this by capturing the entire system in CCSL (Clock Constraint Specification Language). CCSL allows to capture linear but also synchronous constraints. Lastly, the system can be checked for the existence of a schedule satisfying all the constraints using a state space exploration technique. The approach is validated on a typical embedded system application allocated on a multi-core platform.