Volumetric videos offer a more immersive experience and enable a wide range of applications. However, the massive data volume of point clouds in volumetric video streaming typically demands bandwidths in the hundreds of Mbps, often exceeding the capabilities of mobile devices. A key challenge in existing approaches lies in the inefficient transmission of point cloud data. In this paper, we propose IFC, an inter-frame framework for point clouds. IFC leverages adaptive decision-making to evaluate motion estimation (ME) performance and prediction units (PUs) size. Based on this, IFC dynamically partitions PUs and selectively applies inter-frame or intra-frame strategies to optimize coding efficiency. Besides, sparse intra-frame points are clustered to reduce bitstream size while preserving video quality. Experiments on standard dynamic point cloud datasets demonstrate that IFC achieves an average bitrate reduction of 19.25% and 9.98% compared to widely used works G-PCC and GeS-TM v9.0, respectively. Furthermore, compared with GeS TM v9.0, the proposed IFC method achieves an average reduction of 16.8% in total encoding time and 41.68% in decoding time across all sequences over 32 frames. This highlighting IFC’s efficiency and practical potential in real-world and real-time applications.