Simulation Framework for HTTP-Based Adaptive Streaming Applications

The popularity of Internet-based video services has significantly increased over the past years. The de facto standard technology for Internet-based Video on Demand is HTTP-Based Adaptive Streaming (HAS), which is also increasingly used for live services. A core component of a HAS client is the adaptation algorithm, which dynamically adjusts the video representation to the network conditions. Meanwhile, there exists a large body of work on adaptation algorithms. Unfortunately, many experimental studies lack a thorough performance evaluation. Often, the reason is the use of an unrealistic network environment, or incomparability of results with other studies, or a too narrow subset of evaluated parameter configurations. We argue that a simulative approach can help resolving these issues by requiring less efforts to set up a realistic network environment, by assisting to reproduce an experiment, and by allowing to parallelize simulations, and potentially run them faster than in real time. The contribution of the present work is a design and implementation of a simulation model for a HAS-based application, including both the client and a server side. It has a clean modularized structure allowing for an easy integration of different adaptation algorithms. The client behavior is defined by a Finite State Machine that can easily be extended to include additional functionality. Moreover, the model provides extensive logging functionality for monitoring the Quality of Experience (QoE). We integrate three state-of-the-art algorithms into the model: FESTIVE, PANDA, and TOBASCO2. We demonstrate the usefulness of the model by running a set of experiments using a simulated indoor Wi-Fi environment.