Loop fusion and loop shifting are well recognized loop transformations for memory requirement reduction. State-of-the-art optimizations with loop fusion and shifting are based on heuristics without any evaluation of the resulting effects during each optimization step. Thus we cannot guarantee that each step results in a reduced overall memory requirement. On the other hand, most memory requirement estimations at system level are inefficient and slow. Also the estimation is not started until the optimization is done. Having to iterate between optimization and estimation is very time consuming. In this paper, we present a storage requirement optimization method which combines the optimization and estimation processes with the goal to have continuous estimates during the optimization and hence to achieve lower memory requirements.