Server selection is an important problem of cloud computing in which cloud service providers direct user demands to servers in one of the multiple data centers located in different geographical locations. The existing solutions usually assume homogeneity of cloud services (i.e., all users request the same type of service) and handle user demands in an individual basis which incurs high computational overhead. In this study, we propose a new and effective server selection scheme in which diversities of cloud services are taken into account. We focus on a specific cloud service, i.e., online video service, and assume that different videos have different bandwidth requirements. We group users into clusters and handle user demands on a cluster basis for faster and more efficient process. Given user demands and bandwidth capacities of servers in the data centers, our problem is to assign the user demands to the servers under the bandwidth constraint, such that the overall latency (measured by the network distance) between the user clusters and the selected servers is minimized. We design a server selection system and formulate this problem as a linear programming formulation which can be solved by existing techniques. The system periodically executes our scheme and computes an optimal solution for server selection. User demands are assigned to the servers according to the optimal solution and the minimum overall latency can be achieved. The simulation results show that our scheme is significantly better than the random algorithm and the YouTube server selection strategy.