Generalized Instantly Decodable Network Coding for Relay-Assisted Networks

In this paper, we investigate the problem of minimizing the frame completion delay for Instantly Decodable Network Coding (IDNC) in relay-assisted wireless multicast networks. We first propose a packet recovery algorithm in the single relay topology which employs generalized IDNC instead of strict IDNC previously proposed in the literature for the same relay-assisted topology. This use of generalized IDNC is supported by showing that it is a super-set of the strict IDNC scheme, and thus can generate coding combinations that are at least as efficient as strict IDNC in reducing the average completion delay. We then extend our study to the multiple relay topology and propose a joint generalized IDNC and relay selection algorithm. This proposed algorithm benefits from the reception diversity of the multiple relays to further reduce the average completion delay in the network. Simulation results show that our proposed solutions achieve much better performance compared to previous solutions in the literature.Comment: 5 pages, IEEE PIMRC 201

Instantly Decodable Network Coding: From Centralized to Device-to-Device Communications

From its introduction to its quindecennial, network coding has built a strong reputation for enhancing packet recovery and achieving maximum information flow in both wired and wireless networks. Traditional studies focused on optimizing the throughput of the system by proposing elaborate schemes able to reach the network capacity. With the shift toward distributed computing on mobile devices, performance and complexity become both critical factors that affect the efficiency of a coding strategy. Instantly decodable network coding presents itself as a new paradigm in network coding that trades off these two aspects. This paper review instantly decodable network coding schemes by identifying, categorizing, and evaluating various algorithms proposed in the literature. The first part of the manuscript investigates the conventional centralized systems, in which all decisions are carried out by a central unit, e.g., a base-station. In particular, two successful approaches known as the strict and generalized instantly decodable network are compared in terms of reliability, performance, complexity, and packet selection methodology. The second part considers the use of instantly decodable codes in a device-to-device communication network, in which devices speed up the recovery of the missing packets by exchanging network coded packets. Although the performance improvements are directly proportional to the computational complexity increases, numerous successful schemes from both the performance and complexity viewpoints are identified

Coalition Formation Game for Cooperative Content Delivery in Network Coding Assisted D2D Communications

Device-to-device (D2D) communications have shown a huge potential in cellular offloading and become a potential technology in 5G and beyond. In D2D networks, the requested contents by user devices (UDs) can be delivered via D2D links, thus offloading the content providers (CPs). In this work, we address the problem of minimizing the delay of delivering content in a decentralized and partially D2D connected network using network coding (NC) and cooperation among the UDs. The proposed optimization framework considers UDs’ acquired and missing contents, their limited coverage zones, NC, and content’s erasure probability. As such, the completion time for delivering all missing contents to all UDs is minimized. The problem is modeled as a coalition game with cooperative-players wherein the payoff function is derived so that increasing individual payoff results in the desired cooperative behavior. Given the intractability of the formulation, the coalition game is relaxed to a coalition formation game (CFG). A distributed coalition formation algorithm relying on merge-and-split rules is developed for solving the relaxed problem at each transmission. The effectiveness of the proposed solution is validated through computer simulation against existing schemes

Coalition Formation Game for Cooperative Content Delivery in Network Coding Assisted D2D Communications

Device-to-device (D2D) communications have shown a huge potential in cellular offloading and become a potential technology in 5G and beyond. In D2D networks, the requested contents by user devices (UDs) can be delivered via D2D links, thus offloading the content providers (CPs). In this work, we address the problem of minimizing the delay of delivering content in a decentralized and partially D2D connected network using network coding (NC) and cooperation among the UDs. The proposed optimization framework considers UDs’ acquired and missing contents, their limited coverage zones, NC, and content’s erasure probability. As such, the completion time for delivering all missing contents to all UDs is minimized. The problem is modeled as a coalition game with cooperative-players wherein the payoff function is derived so that increasing individual payoff results in the desired cooperative behavior. Given the intractability of the formulation, the coalition game is relaxed to a coalition formation game (CFG). A distributed coalition formation algorithm relying on merge-and-split rules is developed for solving the relaxed problem at each transmission. The effectiveness of the proposed solution is validated through computer simulation against existing schemes

Queue stability analysis in network coded wireless multicast.

In this dissertation queue stability in wireless multicast networks with packet erasure channels is studied. Our focus is on optimizing packet scheduling so as to maximize throughput. Specifically, new queuing strategies consisting of several sub-queues are introduced, where all newly arrived packets are first stored in the main sub-queue on a first-come-first-served basis. Using the receiver feedback, the transmitter combines packets from different sub-queues for transmission. Our objective is to maximize the input rate under the queue stability constraints. Two packet scheduling and encoding algorithms have been developed. First, the optimization problem is formulated as a linear programming (LP) problem, according to which a network coding based optimal packet scheduling scheme is obtained. Second, the Lyapunov optimization model is adopted and decision variables are defined to derive a network coding based packet scheduling algorithm, which has significantly less complexity and smaller queue backlog compared with the LP solution. Further, an extension of the proposed algorithm is derived to meet the requirements of time-critical data transmission, where each packet expires after a predefined deadline and then dropped from the system. To minimize the average transmission power, we further derive a scheduling policy that simultaneously minimizes both power and queue size, where the transmitter may choose to be idle to save energy consumption. Moreover, a redundancy in the schedules is inadvertently revealed by the algorithm. By detecting and removing the redundancy we further reduce the system complexity. Finally, the simulation results verify the effectiveness of our proposed algorithms over existing works

Instantly Decodable Network Coding: From Point to Multi-Point to Device-to-Device Communications

The network coding paradigm enhances transmission efficiency by combining information flows and has drawn significant attention in information theory, networking, communications and data storage. Instantly decodable network coding (IDNC), a subclass of network coding, has demonstrated its ability to improve the quality of service of time critical applications thanks to its attractive properties, namely the throughput enhancement, delay reduction, simple XOR-based encoding and decoding, and small coefficient overhead. Nonetheless, for point to multi-point (PMP) networks, IDNC cannot guarantee the decoding of a specific new packet at individual devices in each transmission. Furthermore, for device-to-device (D2D) networks, the transmitting devices may possess only a subset of packets, which can be used to form coded packets. These challenges require the optimization of IDNC algorithms to be suitable for different application requirements and network configurations. In this thesis, we first study a scalable live video broadcast over a wireless PMP network, where the devices receive video packets from a base station. Such layered live video has a hard deadline and imposes a decoding order on the video layers. We design two prioritized IDNC algorithms that provide a high level of priority to the most important video layer before considering additional video layers in coding decisions. These prioritized algorithms are shown to increase the number of decoded video layers at the devices compared to the existing network coding schemes. We then study video distribution over a partially connected D2D network, where a group of devices cooperate with each other to recover their missing video content. We introduce a cooperation aware IDNC graph that defines all feasible coding and transmission conflictfree decisions. Using this graph, we propose an IDNC solution that avoids coding and transmission conflicts, and meets the hard deadline for high importance video packets. It is demonstrated that the proposed solution delivers an improved video quality to the devices compared to the video and cooperation oblivious coding schemes. We also consider a heterogeneous network wherein devices use two wireless interfaces to receive packets from the base station and another device concurrently. For such network, we are interested in applications with reliable in-order packet delivery requirements. We represent all feasible coding opportunities and conflict-free transmissions using a dual interface IDNC graph. We select a maximal independent set over the graph by considering dual interfaces of individual devices, in-order delivery requirements of packets and lossy channel conditions. This graph based solution is shown to reduce the in-order delivery delay compared to the existing network coding schemes. Finally, we consider a D2D network with a group of devices experiencing heterogeneous channel capacities. For such cooperative scenarios, we address the problem of minimizing the completion time required for recovering all missing packets at the devices using IDNC and physical layer rate adaptation. Our proposed IDNC algorithm balances between the adopted transmission rate and the number of targeted devices that can successfully receive the transmitted packet. We show that the proposed rate aware IDNC algorithm reduces the completion time compared to the rate oblivious coding scheme

Design and analysis of network coding schemes for efficient fronthaul offloading of fog-radio access networks

In the era of the Internet of Things (IoT), everything will be connected. Smart homes and cities, connected cars, smart agriculture, wearable technologies, smart healthcare, smart sport, and fitness are all becoming a reality. However, the current cloud architecture cannot manage the tremendous number of connected devices and skyrocketing data traffic while providing the speeds promised by 5G and beyond. Centralised cloud data centres are physically too far from where the data originate (edge of the network), inevitably leading to data transmission speeds that are too slow for delay-sensitive applications. Thus, researchers have proposed fog architecture as a solution to the ever-increasing number of connected devices and data traffic. The main idea of fog architecture is to bring content physically closer to end users, thus reducing data transmission times. This thesis considers a type of fog architecture in which smart end devices have storage and processing capabilities and can communicate and collaborate with each other. The major goal of this thesis is to develop methods of efficiently governing communication and collaboration between smart end devices so that their requests to upper network layers are minimised. This is achieved by incorporating principles from graph theory, network coding and machine learning to model the problem and design efficient network-coded scheduling algorithms to further enhance achieved performance. By maximising end users' self-sufficiency, the load on the system is decreased and its capacity increased. This will allow the central processing unit to manage more devices which is vital, given that more than 29 billion devices will connect to the infrastructure by 2023 \cite{Cisco1823}. Specifically, given that the limitations of the smart end devices and the system in general lead to various communication conflicts, a novel network coding graph is developed that takes into account all possible conflicts and enables the search for an efficient feasible solution. The thesis designs heuristic algorithms that search for the solution over the novel network coding graph, investigates the complexity of the proposed algorithms, and shows the offloading strategy's asymptotic optimality. Although the main aim of this work is to decrease the involvement of upper fog layers in serving smart end devices, it also takes into account how much energy end devices would use during collaborations. Unfortunately, a higher system capacity comes at the price of more energy spent by smart end devices; thus, service providers' interests and end users' interests are conflicting. Finally, this thesis investigates how multihop communication between end devices influences the offloading of upper fog layers. Smart end devices are equipped with machine learning capabilities that allow them to find efficient paths to their peers, further improving offloading. In conclusion, the work in this thesis shows that by smartly designing and scheduling communication between end devices, it is possible to significantly reduce the load on the system, increase its capacity and achieve fast transmissions between end devices, allowing them to run latency-critical applications