The performance of vision-based control systems, in particular, of highly dynamic vision-based motion control systems, is often limited by the low sampling rate of the visual feedback caused by the long image processing time. In order to overcome this problem, the networked visual servo control (NVSC), which integrates networked computational resources for cloud image processing, is considered in this paper. The main contributions of this paper are the following: 1) a real-time transport protocol for transmitting large-volume image data on a cloud-computing platform, which enables high-sampling-rate visual feedback; 2) a stabilizing control law for the NVSC system with time-varying feedback time delay; and 3) a sending rate scheduling strategy aiming at reducing the communication network load. The performance of the NVSC system with sending rate scheduling is validated in an object-tracking scenario on a 14-DOF dual-arm robot. Experimental results show the superior performance of our approach. In particular, the communication network load is substantially reduced by means of the scheduling strategy without performance degradation.