Research on Quality of Experience (QoE)-based Fog Computing focuses on designing fog computing systems that optimize user-perceived service quality by considering factors such as latency, reliability, throughput, and application responsiveness. This area addresses the challenge of providing superior user experiences in dynamic, heterogeneous, and resource-constrained fog environments. Key research directions include QoE-aware resource allocation and task scheduling, adaptive service provisioning based on real-time user feedback, and predictive modeling of QoE using machine learning and data analytics. Other emerging topics involve multi-objective optimization balancing QoE, energy efficiency, and cost, integration of QoE metrics into edge–fog–cloud collaborative frameworks, and QoE-driven network management for latency-sensitive applications. Additionally, research on context-aware QoE enhancement, user-centric service orchestration, and fault-tolerant QoE-aware frameworks represents significant avenues for advancing responsive, efficient, and user-focused fog computing systems.