Abstract—In recent years, there has been an increasing at- tention on Virtual Reality applications, including 360 immersive videos, which typically consume much more bandwidth than traditional videos. However, only a small part of the produced data (denoted as Field of View or viewport) ends up being watched by the users due to the nature of 360 immersive videos. This negatively impacts the network, through a large increase in traffic and wasted resources. Hence, it is necessary to detect where the user is gazing, and what is the movement of the user’s head, in order to define a viewport-dependent streaming transmission strategy. As there are few datasets providing this information, it is crucial to define an easy-to-run model that generates such information. In this paper, we propose a tile- based simulation approach which can generate the distribution of the user’s behavior, and provides information that can be used to optimize future view-dependent streaming protocols. We first characterize the users’ viewport pattern from datasets gathered from real users by decomposing the 360 stream into tiles and analyzing for each tile its frequency and its time interval distribution. Then, we devise a simulation model by combining a Markov Model for tile transition, and beta distribution for time interval prediction. The results show that the simulation tool is able to characterize the tile sequences of users, which performs closely when compared with the empirical results.