Cloud gaming acceleration faces critical challenges in balancing latency and visual quality. To address these issues, we propose a resource-aware mechanism for fine-grained game analysis, enabling precise identification and management of resource-intensive stages in real-time. By utilizing motion vector (MV) information stored in the encoder, our method enables the edge device to reconstruct and enhance the video frames locally before delivering them to the user. This hybrid architecture not only reduces computational load on the cloud but also minimizes network bandwidth consumption. Experimental results demonstrate that the proposed approach achieves significant improvements in visual quality while maintaining smooth gameplay experiences. Our solution provides a promising direction for optimizing cloud gaming performance by efficiently integrating resource computing with advanced CODEC acceleration techniques.