We consider the problem of enhancing the delivery of real-time traffic in wireless networks using bandwidth sharing between operators. A key characteristic of real-time traffic is that a packet has to be delivered within a delay deadline for it to be useful. The abundance of real-time traffic is evident in the popularity of applications like video and audio conferencing, which increased significantly during the COVID-19 period. We propose a sharing and scheduling policy which involves dynamically sharing a portion of one operator's bandwidth with another operator. We provide strong theoretical guarantees for the policy. We also evaluate its performance via extensive simulations, which show significant improvements of up to 90% in the ability to carry real-time traffic when using the policy. We also explore how the improvements from bandwidth sharing depend on the amount of sharing, and on additional traffic characteristics.

Vehicular ad hoc network (VANET), is a developing platform with massive data demands for infotainment services in recent years. Index Coded NOMA (IC-NOMA) is a spectral efficient transmission method that can be used in VANETs. IC-NOMA applies the concepts of non-orthogonal multiple access (NOMA) over the index coded data to increase spectrum and power efficiency. In NOMA, far user does not get access to the near user data, while near user can successfully decode far user data. Therefore, the IC-NOMA demands a novel design of index code for improved bandwidth efficiency. This work considers the design of index code for NOMA when the user demands in VANET follows the data distribution of one-sided symmetric neighboring consecutive side information single unicast index coding problem (SNC-SUICP). For this setup, we develop an optimal closed form index coding (IC) solution which can bring in additional bandwidth savings through NOMA. The improved performance of the proposed IC-NOMA transmission scheme when compared with one-sided SNC-SUICP in terms of bandwidth efficiency is demonstrated.

5G NR is designed from the ground up to provide support for mission critical communication with ultra-reliability and low latency (URLLC) in addition to other use cases such as enhanced mobile broadband. URLLC allows 5G network performance to approach that of wired networks (e.g., Ethernet) and paves the way for new use cases. In this paper, we focus on the end-to-end design of 5G networks for industrial factory automation. Providing URLLC wireless communication in these environments is especially challenging due to highly dynamic RF variations with possible signal blockage or reflections from moving metal objects. Coordinated Multipoint (CoMP) communication, which leverages spatial diversity to improve reliability without relying on packet retransmission, is a promising approach in these environments. We present a design and performance trade-offs between different CoMP techniques and their implication to capacity, robustness, and architecture. Subsequently, we describe architecture and protocol design aspects that allow 5G to operate as part of a time sensitive network including Ethernet bridging, time synchronization, and QoS. A prototype system that validates certain aspects of the design is also presented.

We consider the problem of optimal rate allocation and admission control for adaptive video streaming sessions in wireless networks with user dynamics. The central aim is to achieve an optimal tradeoff between several key objectives: maximizing the average rate utility per user, minimizing the temporal rate variability, and maximizing the number of users supported. We derive sample path upper bounds for the long-term net utility rate in terms of either a linear program or a concave optimization problem, depending on whether the admissible rate set is discrete or continuous. We then show that the upper bounds are asymptotically achievable in large-scale systems by policies which either deny access to a user or assign it a fixed rate for its entire session, without relying on any advance knowledge of the duration. Moreover, the asymptotically optimal policies exhibit a specific structure, which allow them to be characterized through just a single variable, and have the further property that the induced offered load is unity. We exploit the latter insights to devise parsimonious online algorithms for learning and tracking the optimal rate assignments and establish the convergence of these algorithms. Extensive simulation experiments demonstrate that the proposed algorithms perform well, even in relatively small-scale systems.

We consider the problem of optimal rate allocation and admission control for adaptive video streaming sessions in wireless networks with user dynamics. The central aim is to achieve an optimal tradeoff between several key objectives: maximizing the average rate utility per user, minimizing the temporal rate variability, and maximizing the number of users supported. We identify the structure of algorithms that achieve asymptotically optimal performance in large-capacity systems, and exploit the insight into this structure to devise parsimonious and robust online algorithms. Extensive simulation experiments demonstrate that the proposed online algorithms perform well, even in systems with relatively small capacity.

We consider the problem of optimizing video delivery for a network supporting video clients streaming stored video. Specifically, we consider the joint optimization of network resource allocation and video quality adaptation. Our objective is to fairly maximize video clients' Quality of Experience (QoE) realizing tradeoffs among the mean quality, temporal variability in quality, and fairness, incorporating user preferences on rebuffering and cost of video delivery. We present a simple asymptotically optimal online algorithm, NOVA, to solve the problem. NOVA is asynchronous, and using minimal communication, distributes the tasks of resource allocation to network controller, and quality adaptation to respective video clients. Video quality adaptation in NOVA is also optimal for standalone video clients, and is well suited for use in the DASH framework. Further, NOVA can be extended for use with more general QoE models, networks shared with other traffic loads and networks using fixed/legacy resource allocation.

User perceived video quality depends on a variety of only partially understood factors, e.g., the application domain, content, compression, transport mechanism, and most importantly psycho-visual systems determining the ultimate Quality of Experience (QoE) of users. This paper centers on two key observations in addressing the problem of joint rate adaptation for video streams sharing a congested resource. First, we note that a user viewing a given video will experience temporal variations in the dependence of perceived video quality to the compression rate. Intuitively this is due to the possibly changing nature of the content, e.g., from an action to a slower scene. Thus, in allocating rates to users sharing a congested resource, in particular a wireless system where additional temporal variability in users' capacity may be high, content dependent tradeoffs can be realized to deliver a better overall average perceived video quality. Second, we note that such adaptation of users' rates, may result in temporal variations in video quality which combined with perceptual hysteresis effects will degrade users' QoE. We develop an asymptotically optimal online algorithm, requiring minimal statistical information, for optimizing users' QoE by realizing tradeoffs across mean, variance and fairness. Simulations show that our approach achieves significant gains in viewers' QoE. The novelty of this work lies not only in tackling the fundamental problem of achieving fair allocations of perceived video quality across a user population with time varying sensitivities and capacity, but, in addition, in integrating the deleterious impact that variations in perceived quality has on their QoE.

Multipath flow control has been proposed as a key way to improve the Internet's performance, reliability, and flexibility in supporting changing loads. Yet, at this point, there are very few tools to quantify the performance benefits; particularly in the context of a stochastic network supporting best effort flows, e.g., file transfers and web browsing sessions, where the metric of interest is transfer delay. This paper's focus is on developing analysis tools to evaluate flow-level performance and to support network design when multipath bandwidth allocation is based on proportional fairness. To overcome the analytical intractability of such systems we study closely related multipath approximations based on insensitive allocations such as balanced fairness. We obtain flow-level performance bounds on the mean per bit delay, exhibiting the role of resource pooling in the network, and use these to explore scenarios where increased path diversity need not result in high gains. While insightful these results are difficult to use to drive network design and capacity allocation. To that end, we study the large deviations for congestion events, i.e., accumulation of flows, in networks supporting multipath flow control. We show that such asymptotics are determined by certain critical resource pools, and study the sensitivity of congestion asymptotics to the pool's capacity and traffic loads. This suggests a disciplined approach to a capacity allocation problem in multipath networks based on a linear optimization problem.

We study a sensor node with an energy harvesting source. The generated energy can be stored in a buffer. The sensor node periodically senses a random field and generates a packet. These packets are stored in a queue and transmitted using the energy available at that time. We obtain energy management policies that are throughput optimal, i.e., the data queue stays stable for the largest possible data rate. Next we obtain energy management policies which minimize the mean delay in the queue. We also compare performance of several easily implementable sub-optimal energy management policies. A greedy policy is identified which, in low SNR regime, is throughput optimal and also minimizes mean delay.

We study a sensor node with an energy harvesting source. In any slot, the sensor node is in one of two modes: Wake or Sleep. The generated energy is stored in a buffer. The sensor node senses a random field and generates a packet when it is awake. These packets are stored in a queue and transmitted in the wake mode using the energy available in the energy buffer. We obtain energy management policies which minimize a linear combination of the mean queue length and the mean data loss rate. Then, we obtain two easily implementable suboptimal policies and compare their performance to that of the optimal policy. Next, we extend the Throughput Optimal policy developed in our previous work to sensors with two modes. Via this policy, we can increase the throughput substantially and stabilize the data queue by allowing the node to sleep in some slots and to drop some generated packets. This policy requires minimal statistical knowledge of the system. We also modify this policy to decrease the switching costs.