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

TL;DR

This paper presents WiFlix, an adaptive video streaming protocol for massive MU-MIMO wireless networks that optimizes user QoE through a layered control policy combining DASH-like adaptation and max-weight scheduling.

Contribution

It introduces a novel joint control framework for adaptive video streaming in massive MU-MIMO networks, leveraging Lyapunov optimization and a low-complexity user selection scheme.

Findings

Achieves fair QoE maximization among users.

Develops an efficient, scalable user selection algorithm for massive MIMO.

Demonstrates improved streaming performance in multi-cell wireless environments.

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.