Learned video compression achieves high compression ratios but often suffers from low speeds due to model complexity and high-resolution spatial operations. In this work, we propose an efficiency-optimized framework that emphasizes low-resolution representation learning to accelerate inference. Specifically, we reduce the resolution of reused inter-frame propagated features (including those from I-frames) and employ joint I/P-frame training to enhance feature interaction. Our method efficiently exploits multi-frame priors for parameter prediction with minimal additional decoding computation. Furthermore, we revisit the Online Encoder Update (OEU) strategy to boost compression performance without sacrificing decoding efficiency. Overall, our framework significantly improves the trade-off between compression efficiency and inference speed, achieving performance comparable to VTM-LDP. Compared to DCVC-HEM, it delivers a similar compression ratio while offering \(3\times \) faster encoding and \(7\times \) faster decoding, decoding 1080p frames in under 100ms on an RTX 2080Ti.