This study introduces a lightweight and efficient Video Super-Resolution (VSR) model that leverages codec prior information, focusing on motion. Unlike existing VSR models that aim for superior performance through complex structures, the proposed model is designed to be suitable for videos with significant object motion by conducting extensive training with a large dataset. Input of the model, in line with the real-time nature of video services, is compressed video data. By incorporating compressed information and affine transform considerations into the model structure, we have broadened the utilization of spatial-temporal information. Despite its lightweight parameters and minimal computational resource requirements, the proposed model demonstrates meaningful results when applied to videos with substantial motion. This marks a significant departure from other VSR models that demand extensive parameters and computation time, thereby highlighting the potential for implementing a small yet powerful VSR model with enhanced real-time applicability.