In video-on-demand systems, the I.O bandwidth of video servers is the critical resource which contributes to increase in latency. Several approaches: bridging, piggybacking are used to reduce the I/O demand on the video server through sharing. Batching delays the requests for the different videos for a batching window so that more requests for the same video arriving during the current batching window may be served using the same stream. In this paper, we propose an adaptive reservation-based batching policy which dynamically reserves video server capacity for popoular videos according to video server loads. The performance of the proposed policy is evaluated through a simulation, and is compared with simple batching and static reservation-based batching policies. As the result, we know that the adaptive reservation-based batching policy more improves service ratio and average waiting time than simple batching and simple reservation-based batching policy more improves service ratio and average waiting time than simple batching and simple reservation-based batching polices.