Conventional LiDAR point-cloud compression methods often lead to a decrease in the total number of points and coordinate accuracy of the remaining points. Addressing the limitations of existing optimization methods for point-cloud compression parameters, which frequently overlook the quality loss associated with reducing the number of points, this paper presents a novel approach for the joint optimization modeling of downsampling and quantization parameters in LiDAR point-cloud compression. This method simultaneously tackles both types of losses, thereby improving the compression efficiency of point clouds. Initially, bitstream sizes resulting from compressing point clouds with various parameter pairs are statistically analyzed. Subsequently, an analytical model is developed to elucidate the relationship between the code rate and the pairs of downsampling and quantization parameters. This model is then employed to estimate the minimum distortion of the code rate and the corresponding parameter pairs. Finally, a joint optimization model for downsampling and quantization parameters is formulated based on the relationship between the code rate and the parameter pairs associated with the minimum distortion. The experimental results indicate that the proposed method effectively improves the compression efficiency of point-cloud data. Compared with the baseline encoder, this method achieves a BD-rate improvement of 10.43% on the fit dataset and 16.39% on the test dataset.