The aim of this thesis is to present a new packet prioritization framework for image compression and transmission, optimized for energy constrained mobile computing environment. In Energy constrained environment, an image compression system needs to be designed in such a way that the compressed bits can be scaled down as a trade off to meet the energy constraints, with minimum loss in quality. I propose such an image compression system where both the compression and bit packing are designed in a scalable manner to provide high Quality of Experience (QoE). Quadtree decomposition technique is used for image compression. Using the technique, highly correlated neighboring pixels are represented with fewer bits, and localized pixels with lesser correlation are represented with relatively more bits. The bits are prioritized and compressed according to criteria based on bits per pixel and the corresponding distortion in image reconstruction. Bits that represent more pixels and help in reconstructing the image with minimum distortion are packed with higher priority. This helps achieve minimum quality loss when some lower priority bits are traded off to meet the computing energy constraints. The simulation results are produced to show that the presented scheme can prioritize packets, and provide high QoE, such that it can be scaled for minimum loss in quality in energy constrained mobile computing environment.