Dynamic point cloud compression (DPCC) is crucial in applications like autonomous driving and AR/VR. Current compression methods face challenges with complexity management and rate control. This paper introduces a novel dynamic coding framework that supports variable bitrate and computational complexities. Our approach includes a slimmable framework with multiple coding routes, allowing for efficient Rate-Distortion-Complexity Optimization (RDCO) within a single model. To address data sparsity in inter-frame prediction, we propose the coarse-to-fine motion estimation and compensation module that deconstructs geometric information while expanding the perceptive field. Additionally, we propose a precise rate control module that content-adaptively navigates point cloud frames through various coding routes to meet target bitrates. Experimental results show that our method achieves an average BD-Rate reduction of 5.81\% and a BD-PSNR gain of 0.42 dB over the state-of-the-art method, while maintaining an average bitrate error of 0.40\%. Moreover, the average coding time is reduced by up to 44.6\% compared to D-DPCC, underscoring its efficiency in real-time and bitrate-constrained DPCC scenarios.