Instantly Decodable Network Coding Research Articles

Delay Reduction for Instantly Decodable Network Codes With Lossy Feedback Channels

Delay Reduction for Instantly Decodable Network Codes With Lossy Feedback Channels

Buffering Instantly Decodable Network Coding for a Heterogenous Cellular Network With Dual Interfaces

Buffering Instantly Decodable Network Coding for a Heterogenous Cellular Network With Dual Interfaces

Minimizing the Average Packet Access Time of the Application Layer for Buffered Instantly Decodable Network Coding

In B-IDNC (buffered instantly decodable network coding), each receiver can cache the non-instantly decodable network coded packets (NIDPs) which contain two wanted packets of the network layer for subsequent network decoding, so that more packets can be recovered at the network layer than the traditional instantly decodable network coding (IDNC). By employing B-IDNC, we consider a radio access network wherein a base station (BS) is required to broadcast a block of packets to a set of receivers. After completing network decoding at the network layer, each receiver can deliver its recovered packets from the network layer to the application layer in order. We consider minimizing the average packet access time of the application layer for B-IDNC. For the optimization problem is intractable, we approximate it to reduce the sum minimum access delay of the application layer across all receivers. The approximate problem is shown to be equivalent to a maximum weight encoding clique problem over the B-IDNC graph. We propose a simple heuristic algorithm based on greedy maximum weight vertex search to solve the approximate problem. Simulation results verify the effectiveness of our proposed algorithm as compared with the existing techniques.

On the Packet Decoding Delay of Linear Network Coded Wireless Broadcast Sur le délai de décodage des paquets de la diffusion sans fil codée par réseau linéaire

We apply linear network coding (LNC) to broadcast a block of data packets from one sender to a set of receivers via lossy wireless channels, assuming that each receiver already possesses a subset of these packets (through previous systematic transmissions) and wants the rest. We aim to characterize the average packet decoding delay (APDD), which reflects how soon each data packet can be decoded by each receiver on average, and to minimize it without sacrificing throughput. To this end, we first derive closed-form lower bounds on the expected APDD of LNC techniques. We then prove that determining whether these lower bounds are tight is NP-hard and so is APDD minimization. We then prove that every throughput-optimal LNC technique can approximate the minimum expected APDD with a ratio between 4/3 and 2 and that this ratio is exactly 2 for random LNC (RLNC). We also show that instantly decodable network coding (IDNC) techniques cannot approximate APDD due to suboptimal throughput. Finally, we propose hypergraphic LNC (HLNC), a novel throughput-optimal and APDD-approximating technique based on a hypergraphic model of receivers. Our simulations show that the APDD of HLNC significantly outperforms existing techniques, including RLNC, under all considered settings without any sacrifice on throughput.

Maximum Decoding Clique Based Maximum Weight Vertex Search Algorithm for Buffered Instantly Decodable Network Codes

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.

A Low-Complexity Multicast Scheduling for D2D-Aided F-RANs Using Network Coding

Traditional studies on Instantly Decodable Network Coding (IDNC) focus on optimizing IDNC metrics, i.e., completion time or decoding delay, in broadcast scenarios in which all user-devices (UDs) want the same files. The problem of joint optimization over two interdependent parameters, i.e., transmitting user-devices (UDs) and IDNC was traditionally tackled by finding the set of feasible cooperation between UDs. This, however, requires high computational complexity, which is not feasible. This work, instead, develops low complexity, yet optimal, solution to the problem using graph theory in practical multicast scenarios where UDs are interested in different subsets of files. Specifically, the proposed method studies the condition for generating important UDs’ feasible cooperation that can minimize the IDNC metric under investigation while considering the effect of unwanted files on the performance of IDNC schemes for multicast sessions. Numerical results show that the proposed method outperforms the benchmark method in terms of completion time and decoding delay with significant reduction in the computational complexity.

V2V-CoVAD: A vehicle-to-vehicle cooperative video alert dissemination mechanism for Internet of Vehicles in a highway environment

Nowadays, traffics on the roads are getting more and more congested due to the fast-rising number of vehicles and the increasing need of citizens for mobility. Accidents happen frequently and result great loss of lives and properties. Internet of Vehicles (IoVs), as an important part of Internet of Things (IoTs), is placed high hope for enhancing traffic safety and controlling the damage caused by traffic accidents. According to statistics, most accidents can be avoided if drivers can be notified of a danger timely, so the key to minimize the damage introduced by road traffic accidents is the realization of fast and efficient data dissemination. However, great challenges exist due to the transmission characteristics of IoVs, such as high-speed movement of vehicles and dynamic changing network topologies. In this paper, we propose a vehicle-to-vehicle cooperative video alert dissemination mechanism for transmitting accident video in the highway scenario of IoVs. A two-way cooperative transmission strategy is designed. Vehicles in the same direction of accident vehicle are formed into clusters and communicate within clusters, while vehicles in the opposite direction select relay vehicles to help spreading video fast and reliably. The difficulties brought in by the characteristics of IoVs are solved through careful considerations of multiple factors such as vehicles' speeds, locations, distances, channel conditions and data receiving statuses in the design of the mechanism. Moreover, Scalable Video Coding (SVC) technology is used for encoding the original accident video, to take care of the performance degradation caused by the heterogeneity of vehicles in different locations. Instantly Decodable Network Coding (IDNC) technology is adopted during the cooperative transmission to further improve the transmission efficiency and reliability. Simulation results justify the proposed mechanism that it effectively shortens the accident video transmission delay, increases the success warning ratio, enhances the reconstructed video quality and improves the user satisfaction.

Low-Complexity Scheduling for Delay Minimization in D2D Communications Using Network Coding

In this letter, we consider the decoding delay minimizing problem for delivering a frame of packets to a set of user-devices (UDs) using instantly decodable network coding (IDNC). In the considered device-to-device (D2D) network, UDs have limited coverage zones that represent clusters and can speed up the delivery of the requested packets of other UDs by sending IDNC packets. The decoding delay minimization problem is a joint optimization problem of selecting the transmitting UDs and their coding decisions. In this work, we propose a low complexity, yet optimal, solution to the decoding delay minimization problem using graph pruning method. Our proposed innovative method introduces a sequential pruning algorithm that judiciously generates clusters that are certainly contributing to the network while simultaneously designing a new multi-layer IDNC graph. We also prove that the optimal solution to the problem can be achieved by the generated clusters by our proposed algorithm. Numerical results reveal that the proposed solution significantly reduces the computational complexity compared to the existing method with similar decoding delay performance.

Packet Loss Recovery in Broadcast for Real-Time Applications in Dense Wireless Networks

Packet loss recovery in wireless broadcast is challenging, particularly for real-time applications which have strict and short delivery deadline. To recover the maximum number of lost packets within a short time, existing packet recovery solutions often rely on instantly decodable network coding (IDNC). Some of these solutions can recover nearly the maximum number of lost packets possible at the cost of collecting feedback from all (or a large percentage of) users. This is impractical in dense networks. In addition, their runtime grows with the number of users, which is not desirable due to the urgent delivery deadline of real-time applications. In this work, we introduce random instantly decodable network coding (RIDNC), a random encoding approach to IDNC. We propose RA ndom IDN C E ncoder (RACE), a fast RIDNC encoder that can recover nearly as many lost packets as the optimal RIDNC encoder. We compare RACE with the CrowdWiFi encoder, a high performing packet loss recovery solution used in CrowdWiFi, a commercial system for broadcasting live video in dense networks. We show that RACE is up to two orders of magnitude faster than the CrowdWiFi encoder, and recovers more lost packets in practice, where there is not enough time to collect feedback from many users.

NCaAC: Network Coding-Aware Admission Control for Prioritized Data Dissemination in Vehicular Ad Hoc Networks

Vehicular Ad hoc NETworks (VANETs) are becoming an important part of people’s daily life, as they support a wild range of applications and have great potential in critical fields such as accident warning, traffic control and management, infotainment, and value-added services. However, the harsh and stringent transmission environment in VANETs poses a great challenge to the efficient and effective data dissemination for VANETs, which is the essential in supporting and providing the desired applications. To resolve this issue, Instantly Decodable Network Coding (IDNC) technology is applied to stand up to the tough transmission conditions and to advance the performance. This paper proposes a novel admission control method that works well with any IDNC-assisted data dissemination algorithm, to achieve fast and reliable data dissemination in VANETs. Firstly, the proposed admission control strategy classifies the safety-related applications as high priority and the user-related applications as low priority. It then conducts different admission policies on these two prioritized applications’ data. An artfully designed network coding-aware admission policy is proposed to regulate the flow of low-priority data requests and to prevent the network from congestion, through comparing the vectorized distances between the data requests and the encoding packets. Moreover, the carefully planned admission strategy is benefit for maximizing the network coding opportunities by inclining to admit requests which can contribute more to the encoding clique, thus further enhancing the system performance. Simulation results approve that the proposed admission control method achieves clear advantages in terms of delay, deadline miss ratio, and download success ratio.

An Improved Weight Design for Unwanted Packets in Multicast Instantly Decodable Network Coding

Traditional studies on Instantly Decodable Network Coding (IDNC) focus on broadcast scenarios in which all users are interested in receiving the same packets. The problem is usually formulated as a maximum weight clique search in the IDNC graph for well-designed weights that reflect the metric under investigation, i.e., completion time or decoding delay. These schemes are allegedly extended to multicast sessions by introducing a two-layered IDNC graph wherein the first layer contains Wanted Packets (WPs), and the second one represents UnWanted Packets (UWPs). This letter studies the effect of UWPs on the performance of IDNC schemes for multicast sessions. In particular, the letter derives the condition under which the two-layer structure is optimal and concludes that the approach is the most appropriate in most, but not all, cases. Furthermore, the letter designs weights for UWPs that do not depend on the metric but rather represent their probability of offering future decoding opportunities for WPs. Numerical results reveal that the proposed solution outperforms existing methods for an identical computation complexity.

Cache-Aware Network Coding-Based Scheduling for SVC Multicast in Wireless Networks with Hard Deadline Constraints

To provide multi-resolution video for heterogeneous users in wireless networks, Scalable Video Coding (SVC) encodes a video bit stream into one base layer and several enhancement layers, on the basis of their contribution in recovering the video correspondingly. However, a decoding dependency is introduced into these layers and artful operation is required for SVC streaming. Meanwhile, each SVC packet is associated with a hard time constraint so that users can recover their desired video quality timely. This paper focuses on the network coding assisted SVC multicast with hard deadline constraints. Specifically, we consider the cache-aware model where users are able to store all received packets and decode out the desired packets when sufficient packets are obtained. We analyze the effect of cached network coded packets on the SVC packets scheduling process and the video quality improvement, and value that effect through assigning proper weight for each packet. We also take multiple factors that affect quality of experience into consideration, such as channel condition, request’s deadline and user’s packets receiving status. Moreover, we adopt the instantly decodable network coding (IDNC) technique to schedule SVC packets. According to packets weighting on the vertices of a well-structured two-layered IDNC graph, an effective heuristic algorithm is proposed to schedule the most valuable SVC packets combinations within acceptable complexity. Extensive simulation results verify the effectiveness and the quality of experience enhancement accomplished by the proposed algorithm.

A Coalitional Game-Theoretic Framework for Cooperative Data Exchange Using Instantly Decodable Network Coding

This paper investigates the cooperative data exchange (CDE) scheme using the instantly decodable network coding across energy-constrained devices over the wireless channels. In fact, enabling the CDE brings several challenges, such as how to extend the network lifetime and how to reduce the number of transmissions in order to satisfy the urgent delay requirements. The problem is modeled using the cooperative game theory in partition form. Unlike most existing studies, which are only delay-sensitive, we take into account both the completion time and the consumed energy. We propose a distributed merge-and-split algorithm to allow the wireless nodes to self-organize into the independent disjoint coalitions in a distributed manner. Indeed, the proposed algorithm guarantees reduced energy consumption and minimizes the delay in the resulting clustered network structure. Note that we have considered not only the transmission energy but also the computational energy consumption. Moreover, we focus on the mobility issue and we analyze how, in the proposed framework, the nodes can adapt to the dynamics of the network. Such an important result offers insights into how to design the scalable energy and delay aware CDE framework. The simulation results validate the proposed framework and show that, interestingly, the nodes reduce both the energy consumption and the completion time.

Data Dissemination Using Instantly Decodable Binary Codes in Fog-Radio Access Networks

This paper considers a device-to-device (D2D) fog-radio access network wherein a set of users are required to store/receive a set of files. The D2D devices are connected to a subset of the cloud data centers and thus possess a subset of the data. This paper is interested in reducing the total time of communication, i.e., the completion time, required to disseminate all files among all devices using instantly decodable network coding (IDNC). Unlike previous studies that assume a fully connected communication network, this paper tackles the more realistic scenario of a partially connected network in which devices are not all in the transmission range of one another. The joint optimization of selecting the transmitting device(s) and the file combination(s) is first formulated, and its intractability is exhibited. The completion time is approximated using the celebrated decoding delay approach by deriving the relationship between the quantities in a partially connected network. The paper introduces the cooperation graph and demonstrates that the problem is equivalent to a maximum weight clique problem over the newly designed graph. Extensive simulations reveal that the proposed solution provides noticeable performance enhancement and outperforms previously proposed IDNC-based schemes.

Blind Instantly Decodable Network Codes for Wireless Broadcast of Real-Time Multimedia

Instantly decodable network codes (IDNCs) are suggested in the literature to mitigate the problem of decoding delay in network coding. IDNC is also suggested for broadcast scenarios, where the goal is to maximize the number of decoded packets by the receivers, e.g., in multimedia broadcast. It is shown that after uncoded transmission of all the packets, one coded packet can be instantly decodable by a large number of users, when the transmitter is sighted, i.e., it has a perfect knowledge of the lost packets by each receiver. We introduce and study blind IDNC for broadcast, where the transmitter has no knowledge of the lost packets. Similar to the sighted IDNC, first all data packets are transmitted uncoded. Then, assuming the same erasure rate for all users, we allow a small number of coded packets (one, two or three), and study how these coded packets should be constructed to recover as many lost packets at the receivers as possible. The optimal solutions when one blind packet or two non-overlapping blind packets are transmitted are found. We see that two blind transmissions have comparable performance to a single optimal sighted transmission. Moreover, we prove that three blind transmissions outperform any single sighted transmission.

Network Coding for Backhaul Offloading in D2D Cooperative Fog Data Networks

Future distributed data networks are expected to be assisted by users cooperation and coding schemes. Given the explosive increase in the end-users’ demand for download of the content from the servers, in this paper, the implementation of instantly decodable network coding (IDNC) is considered in full-duplex device-to-device (D2D) cooperative fog data networks. In particular, this paper is concerned with designing efficient transmission schemes to offload traffic from the expensive backhaul of network servers by employing IDNC and users cooperation. The generalized framework where users send request for multiple packets and the transmissions are subject to erasure is considered. The optimal problem formulation is presented using the stochastic shortest path (SSP) technique over the IDNC graph with induced subgraphs. However, as the optimal solution suffers from the intractability of being NP-hard, it is not suitable for real-time communications. The complexity of the problem is addressed by presenting a greedy heuristic algorithm used over the proposed graph model. The paper shows that by implementing IDNC in a full-duplex cooperative D2D network model significant reduction in the number of downloads required from the servers can be achieved, which will result in offloading of the backhaul servers and thus saving valuable servers’ resources. It is also shown that the performance of the proposed heuristic algorithm is very close to the optimal solution with much lower computational complexity.

Video distortion reduction with instantly decodable network coding and Device-to-Device communications

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.

A BPPE Algorithm for Instantly Decodable Network Coding in Wireless Broadcasting

Instantly decodable network coding (IDNC) is a low-cost network coding technique for recovering unattained packets by multiple receivers. Due to its NP-hard nature, finding the optimal IDNC coded packets is intractable. In the well-known graph-based method proposed by Sorour et al. , a maximum weight clique selection problem is formulated as an approximation, and a heuristic algorithm called maximum weight clique (MWC) is proposed in maximizing the clique weight. In this letter, a base policy-based partial enumeration (BPPE) algorithm is proposed in addressing the maximum weight clique selection problem. In particular: 1) the necessity of constructing the IDNC graph as in MWC is eliminated and 2) different enumeration lengths could be chosen, so that a consistent performance-run time tradeoff could be achieved. Simulation results testify the effectiveness of BPPE in addressing the maximum weight clique selection problem over existing algorithms in terms of performance-run time tradeoff.

Rate-Aware Network Codes for Video Distortion Reduction in Point-to-Multipoint Networks

This paper considers a wireless point-to-multipoint network in which a base station needs to broadcast a real-time video sequence to a set of devices with heterogeneous channel capacities. In such a scenario, a packet transmission is successfully received at a given device if the adopted transmission rate is lower than the channel capacity of that device. To reduce the video distortion of all devices before the deadline, this paper employs instantly decodable network coding (IDNC) and formulates the video distortion minimization problem as a Markov decision process. Given that the optimal policy suffers from a high computational complexity, an online maximal clique selection algorithm over a rate-aware IDNC graph is proposed to heuristically select a transmission rate and a packet combination at each transmission. This heuristic reduces the individual video distortions of all devices by incorporating the unequal importance of video packets, the hard deadline, and the various channel capacities into the coding decisions. Furthermore, this heuristic is modified to propose a fairer solution that delivers a good quality video to individual devices regardless of their channel conditions. Simulation results over a real video sequence reveal that the proposed IDNC algorithms improve the received video quality as compared to existing rate-aware IDNC algorithms.

Content-Aware Network Coding Over Device-to-Device Networks

Consider a scenario in which a source broadcasts a common content to a group of cooperating mobile devices that are within proximity of each other. Devices in this group may receive only partial content from the source due to packet losses over wireless broadcast links and these packet losses may differ for different devices. The remaining content missing at each device can then be recovered, thanks to cooperation among the devices by exploiting device-to-device (D2D) connections. In this context, the minimum amount of time that guarantees a complete acquisition of the common content at every device is referred to as the “completion time”. It has been shown that instantly decodable network coding (IDNC) reduces the completion time as compared with no network coding in this scenario. However, for applications such as video streaming, not all packets have the same importance and not all devices are interested in the same quality of content. This problem becomes more interesting and challenging when additional, but realistic constraints, such as strict deadline, bandwidth, or limited energy are added in the problem formulation. We assert that direct application of IDNC in such a scenario yields poor performance in terms of content quality and completion time. In this paper, we propose a novel Content- and Loss-Aware IDNC scheme that improves content quality and network coding opportunities jointly by taking into account the contribution of each packet to the desired quality of service (QoS) as well as the channel losses over D2D links. Our proposed Content- and Loss-Aware IDNC (i) maximizes the quality under the completion time constraint, and (ii) minimizes the completion time under the quality constraint. We demonstrate the benefits of Content- and Loss-Aware IDNC through simulations.