Providing services on demand is a major contributing factor to drive the increasingly development of the software defined network. However, it should supply all the current popular applications before it really attains widespread development. Multiple Description Coding(MDC) video applications, as a popular application in the current network, should be reasonably supported in this novel network virtualization environment. In this paper, we address this issue to assign MDC video application into virtual networks with an efficient centralized algorithm(CAMDV). Since this problem is an NP-hard problem, we design an algorithm that can effectively balance the user satisfaction and network resource cost. Previous work just builds a global multicast tree for each description to connect all the destination nodes by breadth-first search strategy or shortest path tree algorithm. But those methods could not achieve an optimal balance or a high-level user satisfaction. By introducing the hierarchical clustering scheme, our algorithm decomposes the whole mapping procedure into multicast tree construction and multipath description distribution. A serial of simulation experiments show that our centralized algorithm could achieve a better performance in balancing the user satisfaction and average mapping cost in comparison with its rivals. Providing services on demand is a major contributing factor to drive the increasingly development of the software defined network. However, it should supply all the current popular applications before it really attains widespread development. Multiple Description Coding (MDC) video applications, as a popular application in the current network, should be reasonably supported in this novel network virtualization environment. In this paper, we address this issue to assign MDC video application into virtual networks with an efficient centralized algorithm (CAMDV). Since this problem is an NP-hard problem , we design an algorithm that can effectively balance the user satisfaction and network resource cost. Previous work just build a global multicast tree for each description to connect all the destination nodes by breadth-first search strategy or shortest path tree algorithm. But those methods could not achieve an optimal balance or a high-level user satisfaction. By introducing the hierarchi a clustering simulation experiments show that our centralized algorithm could achieve a better performance in balancing the user satisfaction and average mapping cost in comparison with its rivals.