System-level dynamic power management (DPM) schemes in Multiprocessor System on Chips (MPSoCs) exploit the idleness of processors to reduce the energy consumption by putting idle processors to low-power states. In the presence of multiple low-power states, the challenge is to predict the duration of the idle period with high accuracy so that the most beneficial power state can be selected for the idle processor. In this work, we propose a novel dynamic power management scheme for adaptive pipelined MPSoCs, suitable for multimedia applications. We leverage application knowledge in the form of future workload prediction to forecast the duration of idle periods. The predicted duration is then used to select an appropriate power state for the idle processor. We proposed five heuristics as part of the DPM and compared their effectiveness using an MPSoC implementation of the H.264 video encoder supporting HD720p at 30 fps. The results show that one of the application prediction based heuristic (MAMAPBH) predicted the most beneficial power states for idle processors with less than 3% error when compared to an optimal solution. In terms of energy savings, MAMAPBH was always within 1% of the energy savings of the optimal solution. When compared with a naive approach (where only one of the possible power states is used for all the idle processors), MAMAPBH achieved up to 40% more energy savings with only 0.5% degradation in throughput. These results signify the importance of leveraging application knowledge at system-level for dynamic power management schemes.