In this paper, we consider the problem of multiprocessor scheduling for safety-critical streaming applications modeled as acyclic data-flow graphs. To the best of our knowledge, most existing works have proposed periodic scheduling that ignore latency or can even have a negative impact on it: the results are quite far from those obtained under Self-Timed scheduling (STS). In this paper, we introduce a new scheduling policy noted Self-Timed Periodic (STP), which is an execution model combining self-timed scheduling with periodic scheduling. The proposed framework shows that the use of both strategies is possible and that they complement each other, STS improves the performance metrics of the programs, while the periodic model captures the timing aspects. We evaluate the performance of our scheduling policy for a set of 10 real-life streaming applications. We find that in most of the cases, our approach gives a significant improvement in latency compared to the Static Periodic Schedule (SPS), and results which are close to the best case latency of STS.