LAUSR

Recent years have witnessed the emergence and ongoing proliferation of dynamic adaptive streaming over HTTP (DASH 1), which reuses web servers with HTTP communication instead of relying on RTSP/RTP/RTCP-based media server and promises to be capable of automatically tuning to bandwidth dynamics. Aware of its excellent performance, the third generation partnership project (3GPP) long term evolution (LTE) has adopted DASH (with specific codecs and operating modes) for use over mobile wireless networks 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 sure the transmitter to deliver distinct data streams to multiple receivers simultaneously, which provides the choices to opportunistically schedule the preferred receivers each time for a common time-frequency resource. In such a system, one of the major challenges to enhance DASH 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, in this paper, we propose a utility maximization framework (UMF) for DASH application delivered 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 links in order to maximize system-wide DASH performance. We prove the NP-hardness of the scheduling problem and propose a priority search algorithm to provide time-efficient solution. We further incorporate novel rate adaptation on the application layer for the scheduled UEs to dynamically set the requested encoding bitrates to explore the balance between agile responsiveness and shifting smoothness. Extensive system-level simulations with realistic video trace validate the effectiveness of our framework in terms of rate adaptability, playback buffer depletion percentage, and battery energy saving.