The majority of applications, ranging from the low complexity to very multifaceted entities requiring dedicated hardware accelerators, are very well suited for Multiprocessor Systems-on-Chips (MPSoCs). It is critical to understand the general characteristics of a given embedded application: its behavior and its requirements in terms of MPSoC resources.This paper presents a complete method to study the important aspect of memory characteristic of an application. This method spans the theoretical, architecture-independent memory characterization to the quasi optimal static memory allocation of an application on a real shared-memory MPSoC. The application is modeled as an Synchronous Dataflow (SDF) graph which is used to derive a Memory Exclusion Graph (MEG) essential for the analysis and allocation techniques. Practical considerations, such as cache coherence and memory broad-casting, are extensively treated. Memory footprint optimization is demonstrated using the example of a stereo matching algorithm from the computer vision domain. Experimental results show a reduction of the memory footprint by up to 43% compared to a state-of-the-art minimization technique, a throughput improvement of 33% over dynamic allocation, and the introduction of a tradeoff between multi-core scheduling flexibility and memory footprint.