The use of a very large number of antennas at the base station sites (referred to as Massive MIMO) is one of the most promising approaches to cope with the predicted wireless data traffic explosion.Following the current wireless technology trend of moving to higher frequency bands and denser cell deployments, a large number of antennas can be implemented within a small form factor even in smallcell base stations. Envisioned scenarios involve heterogeneous networks (comprised of base stations with different powers, numbers of antennas and multiplexing gain capabilities) serving user traffic with often highly non-homogeneous user density. A key system optimization problem in such networks consists of associating users to base stations such that congestion is avoided and the available wireless infrastructure is efficiently used.In this paper, we consider the user-cell association problem for a massive MIMO heterogeneous network. We formulate the problem as a network utility maximization, where the network utility is a function of the users' long-term average rates (per-user throughputs). Under a massive-MIMO specific system model, we show that optimizing the activity fractions between user-BS pairs problem is a convex problem that can be solved efficiently by centralized sub-gradient algorithms. Furthermore, we show that such a solution is physically realizable, in the sense that there exists a scheduling sequence approaching arbitrarily closely the optimal activity fractions.We also consider a decentralized user-centric scheme, where each user has a positive probability to switch cell association if the utility expected from a different base station is higher than the utility achieved from the currently associated one. We formulate a non-cooperative association game and show that its pure-strategy Nash equilibria must be close to the global optimum of the centralized problem.We also show that, under certain technical conditions that we refer to as heavy-loaded network, if the centralized global optimum consists of a unique association (i.e., no user has positive activity fraction to more than one base station), then this association is a pure-strategy Nash equilibrium of the corresponding user-centric association game. Based on previously known results, we also have that the proposed usercentric decentralized probabilistic scheme converges to a pure-strategy Nash equilibrium with probability 1, for the practically relevant cases of proportional fairness and max-min fairness utility functions. Hence, our user-centric algorithm is attractive not only for its simplicity and fully decentralized implementation, but also because it operates near the system social optimum.

We consider the design of a scheduling policy for video streaming in a wireless network formed by several users and helpers (e.g., base stations). In such networks, any user is typically in the range of multiple helpers. Hence, an efficient policy should allow the users to dynamically select the helper nodes to download from and determine adaptively the quality level of the requested video segment. In order to obtain a tractable formulation, we follow a "divide and conquer" approach: i) We formulate a Network Utility Maximization (NUM) problem where the network utility function is a concave and componentwise nondecreasing function of the time-averaged users' requested video quality index and maximization is subject to the stability of all queues in the system. ii) We solve the NUM problem by using a Lyapunov Drift Plus Penalty approach, obtaining a dynamic adaptive scheme that decomposes into two building blocks: 1) adaptive video quality and helper selection (run at the user nodes); 2) dynamic allocation of the helper-to-user transmission rates (run at the help nodes). Our solution provably achieves NUM optimality in a strong per-sample path sense (i.e., without assumptions of stationarity and ergodicity). iii) We observe that, since all queues in the system are stable, all requested video chunks shall be eventually delivered. iv) In order to translate the requested video quality into the effective video quality at the user playback, it is necessary that the chunks are delivered within their playback deadline. This requires that the largest delay among all queues at the helpers serving any given user is less than the pre-buffering time of that user at its streaming session startup phase. In order to achieve this condition with high probability, we propose an effective and decentralized (albeit heuristic) scheme to adaptively calculate the pre-buffering and re-buffering time at each user. In this way, the system is forced to work in the "smooth streaming regime," i.e., in the regime of very small playback buffer underrun rate. Through simulations, we evaluate the performance of the proposed algorithm under realistic assumptions of a network with densely deployed helper and user nodes, including user mobility, variable bit-rate video coding, and users joining or leaving the system at arbitrary times. Contributions:In order to obtain a tractable formulation, we follow a "divide and conquer" approach, conceptually organized in the following steps: i) We formulate a Network Utility Maximization (NUM) problem [19]-[21] where the network utility function is a concave and componentwise non-decreasing function of the time-averaged users' requested video quality index and the maximization is subject to the stability of all queues in the system. The shape of the network utility function can be chosen in order to enforce some desired notion of fairness across the users [22].ii) We solve the NUM problem in the framework of Lyapunov Optimization [23], using the drift plus penalty (DPP) approach [23]. The obtained solution is...

Abstract-Massive MIMO is expected to play a key role in coping with the predicted mobile-data traffic explosion. Indeed, in combination with small cells and TDD operation, it promises large throughputs per unit area with low latency. In this paper we focus on the problem of balancing the load across networks with massive MIMO base-stations (BSs). The need for load balancing arises from variations in the user population density and is more pronounced in small cells due to the large variability in coverage area. We consider methods for load balancing over networks with small and large massive MIMO BSs. As we show, the distinct operation and properties of massive MIMO enable practical resource-efficient load-balancing methods with nearoptimal performance.

Recently, the way people consume video content has been undergoing a dramatic change. Plain TV sets, that have been the center of home entertainment for a long time, are losing ground to hybrid TVs, PCs, game consoles, and, more recently, mobile devices such as tablets and smartphones. The new predominant paradigm is: watch what I want, when I want, and where I want. The challenges of this shift are manifold. On the one hand, broadcast technologies such as DVB-T/C/S need to be extended or replaced by mechanisms supporting asynchronous viewing, such as IPTV and video streaming over best-effort networks, while remaining scalable to millions of users. On the other hand, the dramatic increase of wireless data traffic begins to stretch the capabilities of the existing wireless infrastructure to its limits. Finally, there is a challenge to video streaming technologies to cope with a high heterogeneity of end-user devices and dynamically changing network conditions, in particular in wireless and mobile networks. In the present work, our goal is to design an efficient system that supports a high number of unicast streaming sessions in a dense wireless access network. We address this goal by jointly considering the two problems of wireless transmission scheduling and video quality adaptation, using techniques inspired by the robustness and simplicity of proportional-integral-derivative (PID) controllers. We show that the control-theoretic approach allows to efficiently utilize available wireless resources, providing high quality of experience (QoE) to a large number of users.Index Terms-HTTP-based adaptive streaming, MPEG-DASH, small-cell wireless networks, streaming media.

We consider the jointly optimal design of a trans mission scheduling and admission control policy for adaptive streaming over wireless device-to-device networks. We formulate the problem as a dynamic network utility maximization and observe that it naturally decomposes into two subproblems: admission control and transmission scheduling. The resulting al gorithms are simple and suitable for distributed implementation.The admission control decisions involve each user choosing the quality of the video chunk asked for download, based on the network congestion in its neighborhood. This form of admission control is compatible with the current video streaming technology based on the DASH protocol over TCP connections. We also consider a mechanism for dropping bits from the transmission queues in order to obtain deterministic bounds on the queueing delays, which determine the number of video chunks that should be pre-fetched in order to guarantee smooth playback without interruptions.

Cellular data traffic almost doubles every year, greatly straining network capacity. The main driver for this development is wireless video. Traditional methods for capacity increase (like using more spectrum and increasing base station density) are very costly, and do not exploit the unique features of video, in particular a high degree of asynchronous content reuse. In this paper we give an overview of our work that proposed and detailed a new transmission paradigm exploiting content reuse, and the fact that storage is the fastest-increasing quantity in modern hardware. Our network structure uses caching in helper stations (femto-caching) and/or devices, combined with highly spectrally efficient short-range communications to deliver video files. For femto-caching, we develop optimum storage schemes and dynamic streaming policies that optimize video quality. For caching on devices, combined with device-to-device communications, we show that communications within clusters of mobile stations should be used; the cluster size can be adjusted to optimize the tradeoff between frequency reuse and the probability that a device finds a desired file cached by another device in the same cluster. We show that in many situations the network throughput increases linearly with the number of users, and that D2D communications also is superior in providing a better tradeoff between throughput and outage than traditional base-station centric systems. Simulation results with realistic numbers of users and channel conditions show that network throughput (possibly with outage constraints) can be increased by two orders of magnitude compared to conventional schemes.

We consider the jointly optimal design of a transmission scheduling and admission control policy for adaptive video streaming over small cell networks. We formulate the problem as a dynamic network utility maximization and observe that it naturally decomposes into two subproblems: admission control and transmission scheduling. The resulting algorithms are simple and suitable for distributed implementation. The admission control decisions involve each user choosing the quality of the video chunk asked for download, based on the network congestion in its neighborhood. This form of admission control is compatible with the current video streaming technology based on the DASH protocol over TCP connections. Through simulations, we evaluate the performance of the proposed algorithm under realistic assumptions for a small-cell network.

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.