Quality of Experience (QoE) and privacy protection are central concerns in modern network technologies, especially with the rise of user-centric applications in Beyond 5G (B5G) and emerging 6G networks. However, as the number of connected devices and the volume of exchanged data increase, protecting user privacy becomes more challenging, especially as stronger confidentiality measures restrict access to the data needed to improve QoE. This creates a persistent trade-off, as telecommunications providers (Telecoms) must balance the need to analyze user behavior for service quality improvements while protecting sensitive data. This survey analyzes the trade-off between privacy and QoE, focusing on how privacy-preserving mechanisms are adapted to the constraints of next-generation communication networks. It highlights network-level impacts such as latency, overhead, scalability in mobile environments, and operational challenges for Mobile Network Operators (MNOs) and Service Providers (SPs). We review prominent techniques such as Federated Learning (FL), Encrypted Traffic Inference, Blockchain, and Differential Privacy (DP), clarifying their distinct roles, strengths, and limitations in managing the privacy-QoE trade-off. It highlights challenges related to network-level impacts such as latency, overhead, and scalability in mobile environments, as well as operational challenges for MNOs and SPs. Furthermore, a synthesis of research issues and potential future research directions is presented.