Video decoding and image processing in embedded systems are subject to strong resource constraints, particularly in terms of memory. List-scheduling heuristics with static priorities (HEFT, SDC, etc.) being the oft-cited solutions due to both their good performance and their low complexity, we propose a method aimed at introducing the notion of memory into them. Moreover, we show that through adequate adjustment of task priorities and judicious resort to insertion-based policy, speedups up to 20% can be achieved. We also show that our technique allows to prevent deadlock and to substantially reduce the required memory footprint compared to classic list-scheduling heuristics. Lastly, we propose a methodology to assess the appropriateness of dynamic scheduling in this context.