Predictive Scheduling for DASH Video Streaming in an Underground Subway System

Abstract

With the rapid increases in network bandwidth, watching videos online has become popular. To cope with dynamic and heterogeneous network conditions, video service providers use dynamic adaptive streaming over hyper-text transfer protocol (DASH) streaming technology to serve content. DASH servers dynamically adjust the steaming rate according to the available bandwidth. In this study, we investigated DASH streaming to passengers on an underground subway system, Taipei Mass Rapid Transit (MRT). We developed a mobile app to measure the bandwidth available to mobile devices on subway trips and compiled our measurements with archive data. We observed that the available bandwidth while entering and leaving stations is usually much higher than that while traveling through tunnels. Hence, the DASH streaming algorithm must adapt the video resolution according to a non- deterministic network bandwidth. We also defined an M-Low optimization problem, where using the minimum resolution in DASH streaming provides an acceptable watching experience. Assuming deterministic bandwidth throughout a subway trip, we developed an M-Lowo algorithm for M-Low optimal DASH video scheduling. Since the available bandwidth is generally non- deterministic, we also designed a predictive scheduling M-Lowp algorithm based on archive data to predict network bandwidth in subways and to adjust video resolution automatically to optimize the video-watching experience. To demonstrate the quality of experience improvement achievable using our M-Lowp algorithm, we employed the bandwidth data measured in Taipei MRT as benchmarks. The results demonstrate shown that M- Lowp scheduling is more effective than previously popular algorithms.