Spatio-Temporal Analysis of Bandwidth Maps for Geo-Predictive Video Streaming in Mobile Environments

Abstract

High quality video streaming is increasingly popular among mobile users especially with the rise of high speed LTE networks. But despite the high network capacity of LTE, streaming videos may suffer from disruptions since the quality of the video depends on the network bandwidth, which in turn depends on the location of the user with respect to their cell tower, crowd levels, and objects like buildings and trees. Maintaining a good video playback experience becomes even more challenging if the user is moving fast in a vehicle, the location is changing rapidly and the available bandwidth fluctuates. In this paper we introduce GeoStream, a video streaming system that relies on the use of geostatistics to analyze spatio-temporal bandwidth. Data measured from users' streaming videos while they are commuting is collected in order to predict future bandwidth availability in unknown locations. Our approach investigates and leverages the relationship between the separation distance between sample bandwidth points, the time they were captured, and the semivariance, expressed by a variogram plot, to finally predict the future bandwidth at unknown locations. Using the datasets from GTube our experimental results show an improved performance.