DASHbed: a testbed framework for large scale empirical evaluation of real-time DASH in wireless scenarios
Recent years have witnessed an explosion of multimedia traffic carried over the Internet. Video-on-demand and live streaming services are the most dominant services. To ensure growth, many streaming providers have invested considerable time and effort to keep pace with ever-increasing users' demand for better quality and stall abolition. HTTP adaptive streaming (HAS) algorithms are at the core of every major streaming provider service. Recent years have seen sustained development in HAS algorithms. Currently, to evaluate their proposed solutions, researchers need to create a framework and numerous state-of-the-art algorithms. Often, these frameworks lack flexibility and scalability, covering only a limited set of scenarios. To fill this gap, in this paper we propose DASHbed, a highly customizable real-time framework for testing HAS algorithms in a wireless environment. Due to its low memory requirement, DASHbed offers a means of running large-scale experiments with a hundred competing players. Finally, we supplement the proposed framework with a dataset consisting of results for five HAS algorithms tested in various evaluated scenarios. The dataset showcases the abilities of DASHbed and presents the adaptation metrics per segment in the generated content (such as switches, buffer-level, P. 1203.1 values, delivery rate, stall duration, etc.), which can be used as a baseline when researchers compare the output of their proposed algorithm against the state-of-the-art algorithms.