cloud computing is nothing but set of services and resources which are offered through internet. Using Cloud computing technology services can be delivered to the user by making use of the technique called virtualization. By making use of statistical multiplexing’s virtualized cloud based services can yield significant cost savings across different applications. Achieving the similar cost savings with the real time services is a challenging task. In this paper we are looking for a lower provide costs for the real time iptv services through virtualized iptv architecture and intelligent time shifting of selected services. Here we are going to consider live TV and video on demand as applications and the deadline that has been associated with each of these two applications will be taken as an profit and effectively multiplexing both the services and a generalized framework will be provided to compute the amount of resources that are required to support multiple services without missing the deadline of any of the services. The problem is to find the best cost function of several cost functions and these cost functions reflect the amount of cost which is required to provide the service. Finding solution to the above problem gives the number of servers which are required at different time instants to support multiples / a multiple / the multiple services. We also implement a simple mechanism of time shifting of scheduled jobs of a simulator and study the reduction in the server load using real traces from an operational iptv network and results show that we are able to reduce the load as predicted by the framework.