This paper investigates memory management for real-time multimedia applications running on a resource-constrained platform. It is shown how a shared memory pool can reduce the total memory requirements of an application comprised of a data-driven chain of tasks with a time-driven head and tail and a bounded end-to-end latency. The general technique targeted at memory-constrained streaming systems is demonstrated with a video encoding example, showing memory savings of about 19%.