Instantly Decodable Network Coding for Multiple Unicast Retransmissions in Wireless Point-to-Multipoint Networks

In this paper, we study the problem of distributing a real-time video sequence to a group of partially connected cooperative wireless devices using instantly decodable network coding (IDNC). In such a scenario, the coding conflicts occur to service multiple devices with an immediately decodable packet, and the transmission conflicts occur from simultaneous transmissions of multiple devices. To avoid these conflicts, we introduce a novel IDNC graph that represents all feasible coding and transmission conflict-free decisions in one unified framework. Moreover, a real-time video sequence has a hard deadline and unequal importance of video packets. Using these video characteristics and the new IDNC graph, we formulate the problem of minimizing the mean video distortion before the deadline as a finite horizon Markov decision process (MDP) problem. However, the backward induction algorithm that finds the optimal policy of the MDP formulation has high modeling and computational complexities. To reduce these complexities, we further design a two-stage maximal independent set selection algorithm, which can efficiently reduce the mean video distortion before the deadline. Simulation results over a real video sequence show that our proposed IDNC algorithms improve the received video quality compared with the existing IDNC algorithms.

In this paper, we propose a scheme of wireless multimedia network coded broadcast retransmission, based on Device-to-Device (D2D) communications, in which there are network coding conflicts and transmission conflicts between multiple terminal devices. For this reason, a novel instantly decodable network coding (IDNC) graph is constructed, where all the feasible coding and non-transmission collision cases are shown in the graph. Based on the constructed IDNC graph, we design the optimal broadcast strategy for batch transmission and sliding window transmission, and propose a heuristic maximum weight network coding selection algorithm (MWSA-NC) to reduce the complexity of the optimal search algorithm. The simulation results show that compared to the traditional scheme, the proposed algorithm can greatly improve the throughput of the system, reduce decoding latency and reduce video stream distortion.

This paper considers the problem of reducing the broadcast delay of wireless networks using instantly decodable network coding (IDNC) based device-to-device (D2D) communications. In D2D-enabled networks, devices help hasten the recovery of the lost packets of devices in their transmission range by sending network coded packets. To solve the problem, the different events occurring at each device are identified so as to derive an expression for the probability distribution of the decoding delay. The joint optimization problem over the set of transmitting devices and the packet combinations of each is formulated. Due to the high complexity of finding the optimal solution, this paper focuses on cooperation without interference between the transmitting users. The optimal solution, in such interference-less scenario, is expressed using a graph theory approach by introducing the cooperation graph. Extensive simulations compare the decoding delay experienced in the Point to Multi-Point (PMP), the fully connected D2D (FC-D2D) and the more practical partially connected D2D (PC-D2D) configurations and suggest that the PC-D2D outperforms the FC-D2D in all situations and provides an enormous gain for poorly connected networks.

This paper considers a heterogeneous network architecture wherein devices use two wireless interfaces to receive packets from the base station and to transmit or receive packets from other devices concurrently. For such a network architecture, this paper focuses on time-critical and order-constrained applications that require quick and reliable in-order decoding of the packets. This paper first introduces the dual delivery delay as a measure of degradation compared with the optimal in-order packet delivery to the devices. It then addresses the minimum delivery delay problem using instantly decodable network coding (IDNC). In particular, the dual interface IDNC graph is constructed to represent all feasible coding opportunities and conflict-free transmissions. Subsequently, the minimum delivery delay problem is shown to be equivalent to a maximum weight independent set selection problem over the dual interface IDNC graph. Simulation results demonstrate that the proposed IDNC algorithm effectively reduces the delivery delay as compared with the existing network coding algorithms. Especially, for a layered video transmission, the proposed solution provides a sequential delivering of video layers to individual devices.

The traditional instantly decodable network coding (IDNC) schemes discard the non-instantly decodable packets (NIDPs), which results in the loss of useful information. In this letter, we consider a network coding scheme named as buffered instantly decodable network coding (B-IDNC) where each receiving node can cache the NIDPs containing two unreceived packets for subsequent network decoding, and give a decoding algorithm by utilizing the maximum decoding clique. The maximum decoding clique is defined as a subset of vertices in the IDNC graph, which can be decoded together. We approximate the average packet decoding delay minimization problem as the maximum weight encoding clique problem, and a heuristic algorithm named as maximum decoding clique based maximum weight vertex (MDC-MWV) searching algorithm is proposed to solve the approximated problem. Simulation results show that our proposed algorithm can reduce the average packet decoding delay compared with the conventional IDNC schemes.

This paper investigates the delay minimization problem for instantly decodable network coding (IDNC) based device-to-device (D2D) communications. In D2D enabled systems, users cooperate to recover all their missing packets. The paper proposes a game theoretic framework as a tool for improving the distributed solution by overcoming the need for a central controller or additional signaling in the system. The session is modeled by self-interested players in a non-cooperative potential game. The utility functions are designed so as increasing individual payoff results in a collective behavior which achieves both a desirable system performance in a shared network environment and the Nash equilibrium. Three games are developed whose first reduces the completion time, the second the maximum decoding delay and the third the sum decoding delay. The paper, further, improves the formulations by including a punishment policy upon collision occurrence so as to achieve the Nash bargaining solution. Learning algorithms are proposed for systems with complete and incomplete information, and for the imperfect feedback scenario. Numerical results suggest that the proposed game-theoretical formulation provides appreciable performance gain against the conventional point-to-multipoint (PMP), especially for reliable user-to-user channels.

Most of Wireless Sensor Networks (WSNs) are usually deployed in hostile environments where the communications conditions are not stable and not reliable. Hence, there is a need to design an effective distributed schemes to enable the sensors cooperating in order to recover the sensed data. In this paper, we establish a novel cooperative data exchange (CDE) scheme using instantly decodable network coding (IDNC) across the sensor nodes. We model the problem using the cooperative game theory in partition form. We develop also a distributed merge-and-split algorithm in order to form dynamically coalitions that maximize their utilities in terms of both energy consumption and IDNC delay experienced by all sensors. Indeed, the proposed algorithm enables these sensors to self-organize into stable clustered network structure where all sensors do not have incentives to change the cluster he is part of. Simulation results show that our cooperative scheme allows nodes not only to reduce the energy consumption, but also the IDNC completion time.

We are concerned with designing feedback-based adaptive Instantly Decodable Network Coding (IDNC) schemes with the aim of minimizing retransmission times in wireless multicast and broadcast scenarios. In this paper, we first analyze the multiple factors influencing the transmission times and give the quantitative formula. Meantime, we introduce the concept of Cache-based Instantly Decodable Network Coding (C-IDNC) that the correctly received undecodable retransmission packets are waited in the cache for a future network decoding opportunity rather than delete. Then, we formulate the retransmission times minimization problem as an max-weight search problem and propose efficient algorithms for finding its optimal solution. Further, we propose a heuristic algorithms with worst case linear execution complexity based on sorting and iteration. Simulation results show that our proposed algorithms significantly reduce the IDNC retransmission times compared to previous works.

In this paper, we investigate the use of instantly decodable network coding (IDNC) for improving two fundamental performance metrics, namely, the completion time (as a measure of throughput) and mean decoding delay, in multicast cooperative data exchange (CDE) systems, where a group of geographically close clients cooperate with each other to obtain their missing packets. Here, an IDNC scheme is used for the transmissions across these clients. We utilize the stochastic shortest path (SSP) technique to study the minimum mean completion time problem. However, since finding the optimum solution is intractable, we use the obtained formulation to draw some theoretical guidelines to heuristically find solutions that can efficiently reduce the completion time. Second, we formulate the minimum mean decoding delay problem as selecting the appropriate maximal clique over well-structured graphs at the clients, and in order to reduce its complexity, we propose a simple heuristic algorithm for it. The effectiveness of our proposed algorithms is verified through extensive simulations and comparisons with existing techniques.

Intra-session network coding has been shown to offer significant gains in terms of achievable throughput and delay in settings where one source multicasts data to several clients. In this paper, we consider a more general scenario where multiple sources transmit data to sets of clients and study the benefits of inter-session network coding, when network nodes have the opportunity to combine packets from different sources. In particular, we propose a novel framework for optimal rate allocation in inter-session network coding systems. We formulate the problem as the minimization of the average decoding delay in the client population and solve it with a gradient-based stochastic algorithm. Our optimized inter-session network coding solution is evaluated in different network topologies and compared with basic intra-session network coding solutions. Our results show the benefits of proper coding decisions and effective rate allocation for lowering the decoding delay when the network is used by concurrent multicast sessions.

In the LTE networks, the multicast services can be transmitted by single frequency network (SFN) mode and point-to-multipoint (PTM) mode, and the unicast services are delivered with point-to-point (PTP) mode. To avoid the network congestion in the LTE networks with the multicast and unicast mixed services, an analytic hierarchy process (AHP) in load balancing is proposed to minimize the demanded radio resources of the maximum load cell. The system model of the demanded radio resources in the maximum load cell is based on AHP. For the same multicast service, the AHP calculates the weight of the demanded radio resources with the three transmission modes of SFN, PTM and PTP. Then the selecting problem of the transmission mode is solved by means of simulated annealing (SA) heuristics. It selects the optimal mode between SFN and PTM for the multicast services. Simulation results show that the proposed AHP in load balancing achieves less demanded radio resources of the maximum load cell than SFN mode for all the multicast services.

In this paper we develop a distributed rate control algorithm for multiple-unicast-sessions when network coding is allowed. Building on our recent flow-based characterization of network coding, we formulate the problem as a convex optimization problem. The formulation exploits pairwise coding possibilities between any pair of sessions, where the objective function is the sum of the utilities based on the rates supported by each session. With some manipulation on the Lagrangian of the formulated problem, a distributed algorithm is developed with no interaction between intermediate nodes, and each source having the freedom to choose its own utility function. The only information required by the source is the weighted sum of the queue length updates of each link, which can be piggy-backed on the acknowledgment messages. In addition to the optimal rate control algorithm, we propose a decentralized pairwise random coding scheme (PRC) that is optimal when a sufficiently large finite field is used for network coding. The convergence of the rate control algorithm is proved analytically and verified by extensive simulations. Simulations also demonstrate the advantage of our algorithm over the state-of-the-art in terms of throughput and fairness.

On bridging theory and practice of inter-session network coding for CSMA/CA based wireless multi-hop networks

The network coding capacity of a single multicast traffic is characterized by the min-cut/max-flow (mcMF) theorem, which can be achieved by random linear network coding (RLNC). Nonetheless, the graph-theoretic characterization for multiple unicast/multicast traffic remains an open problem. This paper proposes and studies a new class of intersession-network-coding schemes: RLNC with selective cancelling (SC), which inherits the complexity advantage of RLNC once the set of selective cancelling edges is decided. A graph-theoretic characterization is provided for the achievable rates of RLNC with SC for the general multiple multicast setting. The findings contain most existing achievability results as special cases, including the mcMF theorem of the single multicast traffic and the existing characterization of pairwise intersession network coding. One prominent feature of the proposed approach is its focus on the achievability analysis with arbitrary network topology, arbitrary inter-session packet-mixing capability, and arbitrary traffic demands, which distinguishes the results from the special case analysis, capacity outer bound constructions, and the pattern-based (butterfly-based) superposition arguments.

This paper deals with two representative unbalanced traffic cases for two-hop wireless relay access systems employing network coding and a slotted ALOHA protocol. Network coding is a recent and highly regarded technology for capacity enhancement with multiple unicast and multisource multicast networks. We have analyzed the performance of network coding on a two-hop wireless relay access system employing the slotted ALOHA under a balanced bidirectional traffic. The relay nodes will generally undergo this unbalanced multidirectional traffic but the impact of this unbalanced traffic on network coding has not been analyzed. This paper provides closed-form expressions for the throughput and packet delay for two-hop unbalanced bidirectional traffic cases both with and without network coding even if the buffers on nodes are unsaturated. The analytical results are mainly derived by solving queueing systems for the buffer behavior at the relay node. The results show that the transmission probability of the relay node is a design parameter that is crucial to maximizing the achievable throughput of wireless network coding in slotted ALOHA on two-hop unbalanced traffic cases. Furthermore, we show that the throughput is enhanced even if the traffic at the relay node is unbalanced.