A comparative study of DASH representation sets using real user characteristics

Adaptive streaming strategies over HTTP allow to serve heterogeneous video users with varying demands. By providing different encoded versions (representations) of each video sequence on the server, clients have the freedom to select a representation that best fits their needs. While the topic of selecting a representation based on a pre-defined set is covered very well in the literature, the problem of how to properly select the representation set stored at the main server is usually an overlooked challenge. In this work, we provide an analysis on how the choice of representations on the server impacts the clients' quality. This is achieved by conducting NS-3 based simulations with a total of 10k users and up to 300 concurrent DASH clients for several recommended sets (e.g., Netflix, YouTube, and Apple), and measuring the experienced quality over a timespan of 24 hours. The results show that under heavy load (at peak hours) there is still room for improvement.