Light-field cameras capture 4-dimensional spatio-angular information of the light field. They provide more helpful multiple viewpoints or sub-apertures for visual analysis and visual understanding than traditional cameras. Optical flow is a common method to get scene structure cues from two images, however, subpixel displacements and occlusions are two inevitable challenges in the optical flow estimation from light-field sub-apertures. In this paper, we develop a light-field flow model, and propose an edge-aware light-field flow estimation framework for joint depth estimation and occlusion detection. It consists of three steps: i) An optical flow volume with sub-pixel accuracy is extracted from sub-apertures by edge-preserving interpolation. Then occlusion regions are detected through consistency checking. ii) Robust light-field flow and depth estimation are initialized by a winner-take-all strategy and a weighted voting mechanism. iii) Final depth map is refined by a weighted median filter based on guided filter. Experimental results demonstrate the effectiveness and robustness of our method.