In the field of Video Super-Resolution (VSR), recurrent structures are frequently used in the designed network architectures, yet they face significant challenges in effective information transmission and accurate alignment. To address this issue, this study introduces a concise and efficient network, named Enhanced Spatial Adaptive Fusion Network (ESAFN), to enhance information flow and accuracy in VSR. The core strategy of ESAFN involves a Coupled Propagation Spatially Adaptive Module (CPSAM) and an Implicit Alignment Mechanism (IAM), innovatively restructuring the classical BasicVSR architecture. The proposed method initially promotes the effective integration of forward and backward information, then it dynamically selects the most repr esentative features across multiple scales, and further explores contextual information through Spatially Mixed Convolution (SMC) technology. With the implicit alignment strategy, our method significantly enhances the capability to restore high-frequency details in super-resolved videos, markedly surpassing current advanced technologies.