Predictive prefetching for MPEG DASH over LTE networks

Recently HTTP based adaptive video streaming has gained significant attention. A video client predicts the future available bandwidth and then selects the appropriate bit rate for the video content in order to maximize the video quality while avoiding rebuffering. The often severe fluctuation in the available bandwidth in mobile wireless networks poses a challenge to the bit rate selection algorithm. In this paper, we propose to utilize the regularity in a user´s movement to improve the bit rate selection algorithm. We use Kalman filters to predict a user´s speed and available bandwidth, which are then fed into a bit rate adaptation algorithm. The proposed method is implemented in JavaScript, and tested in real LTE networks. Experimental results show that our method significantly improves the quality of DASH-based video streaming.