WiFlix: Adaptive Video Streaming in Massive MU-MIMO Wireless Networks

Abstract

We consider the problem of simultaneous on-demand streaming of stored video to multiple users in a multi-cell wireless network where multiple unicast streaming sessions are run in parallel and share the same frequency band. Each streaming session is formed by the sequential transmission of video "chunks", such that each chunk arrives into the corresponding user playback buffer within its playback deadline.We formulate the problem as a Network Utility Maximization (NUM) where the objective is to fairly maximize users' video streaming Quality of Experience (QoE) and then derive an iterative control policy using Lyapunov Optimization, which solves the NUM problem up to any level of accuracy and yields an online protocol with control actions at every iteration decomposing into two layers interconnected by the users' request queues : i) a video streaming adaptation layer reminiscent of DASH, implemented at each user node; ii) a transmission scheduling layer where a max-weight scheduler is implemented at each base station. The proposed chunk request scheme is a pull strategy where every user opportunistically requests video chunks from the neighboring base stations and dynamically adapts the quality of its requests based on the current size of the request queue. For the transmission scheduling component, we first describe the general max-weight scheduler and then particularize it to a wireless network where the base stations have multiuser MIMO (MU-MIMO) beamforming capabilities. We exploit the channel hardening effect of large-dimensional MIMO channels (massive MIMO) and devise a low complexity user selection scheme to solve the underlying combinatorial problem of selecting user subsets for downlink beamforming, which can be easily implemented and run independently at each base station. Further, through simulations, we show that deploying MU-MIMO significantly improves video streaming performance and also that the proposed cross-layer approach is able to serve users more fairly than a baseline scheme representative of current systems running independently designed protocol layers.

A survey on QoE-oriented wireless resources scheduling

“…The works presented in (Xiang et al, 2017;Fan and Zhao, 2018) developed cross-layer resource allocation schemes for two-hop and ad hoc networks, respectively, where the goal of both works is to enhance the QoE by minimizing the end-toend video delivery time. In (Bethanabhotla et al, 2016), an efficient system is proposed for video streaming over a wireless system composed by a large amount of wireless helper equipments with multi-user MIMO capabilities, where QoE metrics like video quality and rebuffering percentage are used to optimize the transmission scheduling of users at each base station. Other examples of QoE-based scheduling for multi-user MIMO systems are given in (Cao et al, 2012;Chen et al, 2017), where user selection procedures based on transmitted rate and delay are proposed in order to maximize the average multi-service satisfaction degree.…”

“…Uplink (Essaili et al, 2011;Wu et al, 2012;Condoluci et al, 2017;Liu et al, 2018;Ranjan et al, 2018) Multi-cell (Zheng et al, 2014;Cho et al, 2015;Kim et al, 2015;Miller et al, 2015) Heterogeneous networks (Toseef et al, 2011;Jailton et al, 2013;Seyedebrahimi and Peng, 2015;Morel and Randriamasy, 2017;Abbas et al, 2017) Device-to-device communications (Zhu et al, 2015a;Hong et al, 2017;Biswash and Jayakody, 2018) Vehicular networks (Xu et al, 2013;Yaacoub et al, 2015;Ding et al, 2018) Cognitive radio networks (Jiang et al, 2012;Wu et al, 2013;He et al, 2016;Zhang et al, 2017;Piran et al, 2017;Lin et al, 2017) Relay networks (Reis et al, 2010;Wu et al, 2013;Bethanabhotla et al, 2016;Xiang et al, 2017;Fan and Zhao, 2018) Multi-user MIMO networks (Cao et al, 2012;Bethanabhotla et al, 2016;Chen et al, 2017;Huang and Zhang, 2018) Base stations energy consumption (Ma et al, 2012;Li et al, 2012;Gabale and Subramanian, 2014;Draxler et al, 2014;Sapountzis et al, 2014;…”

CLEVER: A Cooperative and Cross-Layer Approach to Video Streaming in HetNets

“…Focusing on time complexity, CLEVER initially computes the requested bandwidth B N ). In the final part (lines [15][16][17][18][19][20][21][22][23][24], the MAD routine is run for the users that are offloaded to the MeNB. Since the ϕ parameter appearing in Eq.…”

“…As for the implementation of CLEVER in a real system, we notice that several papers propose to use the application layer information typically adopted by the video entity (client/server) at lower layers. The paper [16] proposes, like in our case, the use of multiple nodes (named helpers) that serve multiple wireless users over a given geographic coverage area in a dynamic adaptive video context. Also in their case they adopt a cross-layer approach where the information that needs to be exchanged between the layers is the length of the users request queues, together with the chunk requests.…”

Adaptive Video Streaming for Wireless Networks with Multiple Users and Helpers

“…Of course, for more complicated type of wireless physical layers, the description of R(t) and therefore the solution of the corresponding MWSR problem (16) may be much more involved than in the cases treated here. For example, an extension of this approach to the case of multiantenna helper nodes using multiuser MIMO is given in [40].…”