Interactive scheduling for mobile multimedia service in M2M environment

Computational load of motion estimation in advanced video coding (AVC) standard is significantly high and its more true for HDTV sequences. In this paper, video processing algorithm is mapped onto a learning method to improve machine to machine (M2M) architecture, namely, the parallel reconfigurable computing (PRC) architecture, which consists of multiple units, First, we construct a directed acyclic graph (DAG) to represent the video coding algorithms comprising motion estimation. In the future trillions of devices are connected (M2M) together to provide services and that time power management would be a challenge. Computation aware scheme for different machine is reduced by dynamically scheduling usage of multi-core processing environment for video sequence depending up complexity of the video. And different video coding algorithm is selected depending upon the nature of the video. Simulation results show the effectiveness of the proposed method.