Federated learning has emerged as a promising approach for distributed machine learning, particularly in the edge-cloud infrastructures, where clients (i.e., edge devices) collaboratively train models while maintaining data privacy. However, the dynamic heterogeneity induced by the FEderated Edge Learning (FEEL) environment poses many performance challenges. Indeed, real-world edge devices are considered heterogeneous, where both system resources and data distributions fluctuate significantly over time across edge clients. Thus, selecting the most contributing and resource-efficient clients is crucial in determining the computational efficiency and performance of the overall FEEL system. We propose FedCDRP, an efficient and adaptive Class Diversity and Resource Performance-aware client selection strategy, founded upon our FedCD-CS and FedRP-CS methodologies. Our approach prioritizes clients based on two factors: their resource capabilities and the diversity of their local data. Specifically, FedCD-CS tackles the imbalanced non-I.I.D. data by considering the richness of the local data of each edge device, to enhance the overall learning system by selecting the most contributing clients. Additionally, based on a real-time cross-dimensional system performance profiling, FedRP-CS efficiently assesses the system resources of each edge device, to guarantee the selection of the most resource-efficient clients to effectively manage the computational needs. By combining these factors, our FedCDRP strategy identifies clients that can contribute significantly to the global model update while maintaining computational efficiency in dynamic heterogeneous environments. Through real-world circumstances, the conducted experiments demonstrated the computational efficiency of our FedCDRP strategy, compared to the-state-of-the-art protocols, achieving a 2.9-fold speedup in reaching the target accuracy of 99%.