With the increasing demand for real-time video processing in intelligent environments, optimising energy consumption while maintaining video quality remains a challenge. This paper presents a rule-based adaptive energy optimization framework for video compression, integrating dynamic decision-making techniques to regulate computational complexity based on system constraints. The proposed method employs an energy-aware loss function that dynamically adjusts key parameters based on inference conditions, real-time resource availability, and perceptual video quality. The model autonomously balances compression quality and energy efficiency by leveraging a rule-based approach, ensuring optimal trade-offs in resource-constrained devices. Experimental results demonstrate significant improvements in energy-aware video transmission, achieving adaptive complexity modulation with minimal loss in perceptual quality.