Utility-Maximization Framework for Dynamic Adaptive Streaming over HTTP in Multiuser-MIMO LTE Networks

Dr. Miao Zhao
Huawei Technologies


Recent years have witnessed the emergence and ongoing proliferation of dynamic adaptive streaming over HTTP (DASH), which reuses popular 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 to achieve good visual experience. 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, by utilizing spatial reuse, it is possible for a transmitter to successfully deliver distinct data streams to multiple receivers simultaneously, which provides the choices to opportunistically schedule multiple preferred receivers each time for a common network resource. In such a system, one of the major challenges to enhance the 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 user equipments (UEs). To this end, in this work, we propose a novel scheduling framework for DASH over MU-MIMO LTE downlink. Network resources in time, frequency and spatial domains are periodically assigned to the scheduled UEs in proper granularity with the purpose of maximizing the system-wide network utility for DASH applications. In particular, we characterize the DASH performance by a combined utility function in terms of average video rate, playback buffer status, and battery energy state. Base on the utility-maximization framework, we first drive the utility-based scheduling policy by greatest ascent method. Then, we prove this scheduling problem to be with NP-hard complexity and correspondingly develop a greedy search algorithm to provide practically good solution to the problem. We further incorporate rate adaptation at the scheduled UEs to adaptively set the requesting encoding bitrates by taking into account the impacts of sustainable link capacity, buffer status, and battery state so as to achieve agile and smooth video rate adaptation. Our design strives to enhance the video delivery performance by requesting the MAC layer to have more knowledge about link states and video application features, which is therefore essentially a form of cross-layer design. In the rest of my talk, I will also briefly mention some other works on the topic of mobile data gathering in rechargeable wireless sensor networks, which is the main focus during my PhD study. Finally, I will discuss some possible future research works. Everyone is greatly welcomed!