Large Vision-Language Models (LVLMs) have achieved remarkable success in video understanding tasks, yet they suffer from substantial computational overhead due to the large number of visual tokens generated from video frames. A key observation is that consecutive video frames often contain significant temporal redundancy, especially in static or slowly-changing scenes. In this paper, we propose MotionKV, a training-free, motion-aware KV cache compression framework that selectively retains key-value pairs corresponding to high-motion frames while compressing those from static regions. Our method computes per-frame motion scores using pixel-level difference analysis, then applies a hybrid selection strategy that combines motion-based importance with background-aware penalties to guide token-level retention. Extensive experiments on three video question-answering benchmarks demonstrate that MotionKV retains 12.5% of visual tokens while maintaining 98.8% of full-cache accuracy on VideoMME (Qwen2-VL-7B), achieving 3.1 × inference speedup with 68% reduction in peak memory. The approach is model-agnostic and requires no fine-tuning, enabling seamless integration into existing LVLMs.