We describe a compositional simulation system, which predicts streamed video performance on multiple platform configurations. System behavior is modeled with a set of key deterministic and stochastic variables, each of which characterizes part of a 'virtual system component,' e.g., an IO device, a particular CPU, a codec, etc. These variables are profiled in isolation, by inserting lightweight instrumentation code into the main threads and upcalls involved in the playout datapath. Then, a post-processor converts the derived samples into synthesized probability distribution functions, after which the results are stored in the simulator's library. At simulation time, the user selects a set of 'virtual components,' which are then composed into an 'end-user system.' The resulting model is used to predict the performance of any video, which usually requires no more than a few seconds. The output is a list of frame-display times, accompanied by statistics on the mean-playout rate, variance, jitter, etc. This scheme lets developers extend the range of their target platforms, by automatically benchmarking new components, storing the results, and then simulating an entirely new set of systems. So, if a developer possesses a large set of pre-profiled components, he or she can quickly estimate a video's performance on a huge spectrum of target platforms -- without ever having to actually assemble them. In this paper we demonstrate evidence that our method works within a reasonable degree of accuracy, when compared to actual on-line playout. We present results for a generic, streamed Quicktime video system -- subjected to multiple configurations. These were assembled (combinatorially) using four different CPUs, three types of SCSI devices, two common codecs (Radius Cinepak and Intel Indeo), and two full-frame video masters. On most configurations tested, the simulator's on-line predictions were accurate within a margin of 15% error.