In video super-resolution reconstruction, traditional methods often fall short in capturing details, particularly in edges and occluded areas, which affects the realism and clarity of the images. To address this issue, we propose a novel model-the Residual Hybrid Attention-Enhanced Video Super-Resolution Model, augmented by Cross Convolution techniques, denoted as RCVSR. The model ingeniously integrates a residual hybrid attention mechanism, refining the learning of global and local features through parallel channel attention and self-attention mechanisms. Simultaneously, our model introduces overlapping cross-attention blocks to enhance dynamic interactions between frames, thereby boosting the model’s performance. Furthermore, the design of the cross-convolution blocks allows for parallel processing of vertical and horizontal gradient information in images, effectively extracting edge details. In multiple benchmark tests, the RCVSR model demonstrated its excellent reconstruction effects and outstanding performance.