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This paper investigates memory management for real-time multimedia applications running on resource-constrained electronic devices. The target applications are comprised of a data-driven task chain with a time-driven head and tail and a bounded end-to-end latency. The necessary buffer capacities along the task chain are derived. Subsequently it is shown how a shared memory pool can reduce the total memory requirements of the whole application. The impact of a shared memory pool is also evaluated in the context of scalable applications. The general technique targeted at memory-constrained streaming systems is demonstrated with a video encoding example, showing memory savings of about 19%.