With the rapid expansion of mobile edge computing (MEC), ensuring high-quality service (QoS) for diverse applications has become a critical challenge. This project focuses on developing a context-aware and adaptive QoS prediction framework for mobile edge communication services. By leveraging real-time network conditions, user mobility patterns, and application-specific requirements, the proposed system utilizes machine learning and deep learning models to predict QoS metrics dynamically. The adaptive approach allows proactive resource allocation, reducing latency, optimizing bandwidth utilization, and enhancing user experience in edge environments. Context-aware data processing ensures that predictions remain accurate despite changing network conditions, while the system continuously learns from historical patterns for improved performance. This research contributes to the advancement of intelligent edge computing solutions, enabling seamless and efficient communication services for next-generation mobile networks.
