Quality of Experience (QoE)-based edge computing has become a significant research direction, focusing on optimizing user satisfaction while delivering latency-sensitive and resource-intensive applications over distributed edge infrastructures. Research papers in this field examine how QoE metrics—such as response time, video playback quality, reliability, energy efficiency, and seamless mobility—can be integrated into resource management, service orchestration, and workload offloading strategies. Studies highlight frameworks that dynamically adapt computation, networking, and storage resources based on real-time user experience feedback, often leveraging machine learning and deep reinforcement learning for predictive QoE optimization. Recent works explore QoE-aware edge solutions for applications like immersive media (AR/VR), cloud gaming, mobile video streaming, smart healthcare, and autonomous driving, where user perception directly impacts system performance. Additionally, research emphasizes multi-objective optimization, balancing trade-offs between QoE, Quality of Service (QoS), energy consumption, and provider profit. Emerging works also integrate blockchain, federated learning, and digital twins to enhance trust, personalization, and real-time QoE monitoring in heterogeneous environments. Overall, QoE-based edge computing research demonstrates the shift from purely system-centric optimization to user-centric design, ensuring that edge services meet both technical and experiential expectations.