Cell-Cluster Network-Assisted Adaptive Streaming Media optimization over Wireless Network

Mobile streaming media on subways or trains has become a typical and popular scenario in daily life. However, because of the unstable wireless network channel and fluctuations caused by movement, the constant bit rate streaming media brings low-quality service. The adaptive streaming media seems to solve the problems, but the premise is that clients can accurately estimate the network bandwidth. When the client moves at high speed, the irregular fluctuations of network channel make accurate estimation difficult, leading to the low efficiency of the adaptive streaming media. When clients move on subways or trains, the location, speed and the cell load can be known by the wireless network side. With this information, the available network bandwidth can be accurately calculated in advance. Thus, we propose an architecture called Cell-Cluster Network-Assisted Adaptive Media Streaming (CCNA) to optimize the vehicular streaming media over wireless network. We build a cell cluster to introduce the assistance of the wireless network side and estimate the network bandwidth more accurately. The architecture can help clients select the video segment with the highest QoE under the constraint of limited network bandwidth and make the best use of the network bandwidth resources. The experimental results show that compared with traditional adaptive streaming media transmission modes, our architecture improves the average playback bit rate by 32% and the utilization of network bandwidth by 34% at the same time with the least video stalls.