Quality degradation from video compression manifests both spatially along texture edges and temporally with continuous motion changes. Despite recent advances, extracting aligned spatiotemporal information from adjacent frames remains challenging. This is mainly due to limitations in receptive field size and computational complexity, which makes existing methods struggle to efficiently enhance video quality. To address this issue, we propose RivuletMLP, an MLP-based network architecture. Specifically, our framework first employs a Spatiotemporal Dynamic Alignment (STDA) module to adaptively explore and align multi-frame feature information. Subsequently, we introduce two modules for feature reconstruction: a Spatiotemporal Flow Module (SFM) and a Benign Selection Compensation Module (BSCM). The SFM establishes non-local dependencies through an innovative feature permutation mechanism, recovering details while reducing computational cost. Additionally, the BSCM utilizes a collaborative learning strategy in both spatial and frequency domains to alleviate compression-induced inter-frame motion discontinuities. Experimental results demonstrate that RivuletMLP achieves superior computational efficiency while maintaining powerful reconstruction capabilities.