Using Informed Access Network Selection to Improve HTTP Adaptive Streaming Performance

As end-user devices often have multiple access networks available, choosing the most suitable network can help to improve application performance and user experience. However, selecting the best access network for HTTP Adaptive Streaming (HAS) is non-trivial, e.g., due to complex interactions between network conditions and the Adaptive Bit-Rate algorithm (ABR), which adapts to network conditions by selecting which video representation to load. In this paper, we propose to use an application-informed approach, Informed Access Network Selection (IANS), to select the most suitable access network for each video segment. We evaluate the impact of TANS on HAS performance in a testbed under a variety of network conditions and using different workloads. We find that TANS improves HAS performance substantially, in particular in cases where the available downstream capacity is low. In the Capacity Decrease scenario, where capacity decreases drastically during the video load, IANS can improve the estimated Mean Opinion Score (MOS) compared to using a single network from 2.1 to 2.8. We compare IANS to MPTCP using the Lowest-RTT-first scheduler, which continues to use a low downstream capacity network, resulting in lower performance. This confirms that IANS can improve video streaming performance.