H.264 is a computationally intensive video codec striving for achieving the best quality for the compressed video. The computational complexity poses as a challenge for power-constrained applications. We present a system level complexity reduction for H.264 video encoding by allocating resources based on computational complexity and quality trade-off. We develop a framework which allocates the computational power of the encoder adaptive to video contents and also scales with the available battery power using a ROI classification method. Analysis is done to profile the key modules of the encoder which can be power-optimized while allocating resources. The results of the encoder module analysis are combined with the motion content analysis to obtain a power efficient encoder parameter set which reduces the computations and hence the power consumed. Our simulation results on the JM H.264 framework confirm our hypothesis and computational savings of more than 50% with quality degradation less than 1% is achieved thereby extending it's feasibility for battery powered wireless devices.