Commercial Unmanned Aircraft Systems (UAS) have a wide range of applications, including package delivery, inspection as well as search and rescue missions. To operate Unmanned Aircraft Vehicles (UAV) Beyond Visual Line of Sight (BVLOS), reliable long-distance communication is essential. Although the cellular network is a potential solution, it still faces issues such as signal loss and increased handover at higher altitudes. To mitigate these issues, our work proposes the usage of two cellular links from different providers, which are prioritised according to Quality of Service (QoS) prediction. We evaluate multiple AI-based model architectures for the prediction, and find that the model consisting of Gated Recurrent Units (GRU) and convolutional layers outperforms the others. The models are trained and tested on real-world data showing a reduction in latency peaks, thereby increasing connection resilience. Additionally, the prediction pipeline is designed to be executable on the UAV side and is not limited to a specific geographical area, making it applicable to real-world scenarios. Finally, we present a pre-flight path planning algorithm that considers QoS when calculating the flight path in order to further enhance the communication. To support the research community, we publicly share the dataset used to obtain our results.