Utility-maximization framework for dynamic adaptive streaming over HTTP in multi-user MIMO LTE networks

Recent years have witnessed the emergence and ongoing proliferation of dynamic adaptive streaming over HTTP (DASH¹), which reuses web servers with HTTP communication instead of relying on RTP-based media server and promises to be capable of automatically tuning to bandwidth dynamics. The third generation partnership project (3GPP) long term evolution (LTE) has adopted DASH for use in order to realize ubiquitous multimedia delivery. In a multi-user multiple-input-multiple-output (MU-MIMO) LTE system, spatial multiplexing gain can be achieved by making the transmitter deliver distinct data streams to multiple receivers simultaneously, which provides choices to schedule preferred receivers for a common resource. In such a system, one of the major challenges to enhance adaptive HTTP streaming performance is to design an effective scheduler that can fully enjoy the benefit of spatial reuse as well as guaranteeing satisfactory video services for all users. To this end, we propose a utility maximization framework (UMF) for DASH application over MU-MIMO LTE downlinks. In particular, we characterize DASH performance by a combined utility function in terms of average video rate, playback buffer status, and battery energy state. Correspondingly, we develop a utility-based scheduler that selects multiple user equipments (UEs) to share each common network resource under the consideration of precoding-based MU-MIMO in order to maximize system-wide DASH performance. Specifically, we propose a priority search algorithm to provide time-efficient solution to the scheduling problem. We further incorporate novel rate adaptation on the application layer for scheduled UEs to dynamically set the requested video encoding bitrates. Extensive system-level simulations validate the effectiveness of UMF in terms of rate adaptability, playback buffer depletion percentage and battery energy consumption.