Mohammad S. Karim

Delay Reduction in Persistent Erasure Channels for Generalized Instantly Decodable Network Coding

In this paper, we consider the problem of minimizing the decoding delay of generalized instantly decodable network coding (G-IDNC) in persistent erasure channels (PECs). By persistent erasure channels, we mean erasure channels with memory, which are modeled as a Gilbert-Elliott two-state Markov model with good and bad channel states. In this scenario, the channel erasure dependence, represented by the transition probabilities of this channel model, is an important factor that could be exploited to reduce the decoding delay. We first formulate the G-IDNC minimum decoding delay problem in PECs as a maximum weight clique problem over the G-IDNC graph. Since finding the optimal solution of this formulation is NP-hard, we propose two heuristic algorithms to solve it and compare them using extensive simulations. Simulation results show that each of these heuristics outperforms the other in certain ranges of channel memory levels. They also show that the proposed heuristics significantly outperform both the optimal strict IDNC in the literature and the channel-unaware G-IDNC algorithms.

Decoding delay reduction in broadcast erasure channels with memory for network coding

This paper studies feedback based instantly decodable network coding with the aim of minimizing decoding delay per transmission over wireless broadcast erasure channels with memory. We model such channels with a Gilbert-Elliott two-state Markov model with good and bad states. We first present a weighted sum generalized instantly decodable network coding (G-IDNC) scheme, where the aim is to service a subset of receivers with expected good channel state. We then propose an improved variation of the weighted sum G-IDNC that appropriately targets a broader set of receivers (while giving initial priority to receivers with expected good channel state) to reduce decoding delay over a wider range of erasure channels with memory. Simulation results show that our proposed improved weighted sum G-IDNC algorithm always considerably outperforms an earlier approach in the literature for erasure channels with memory, namely the weighted sum strict instantly decodable network coding (S-IDNC).

Decoding delay reduction in network coded cooperative systems with intermittent status update

In this paper, we study the problem of decoding delay reduction for instantly decodable network coding (IDNC) in broadcast cooperative systems, where a group of closely located clients cooperate with each other to obtain their missing packets. In such cooperative systems, one of the clients (referred to as the leader) decides the transmitting client and the packet combination for each transmission. We consider intermittent system status update (SSU) at the leader such that all other clients feed back their packet reception status to the leader after several cooperative transmissions. We first introduce an intermittent local IDNC (IL-IDNC) graph to represent all potential packet combinations for a transmitting client. We then formulate the joint client and packet selection problem that results in the minimum expected decoding delay in each cooperative transmission as a maximum weight clique problem over all the IL-IDNC graphs. Since solving the formulated problem is computationally complex, we propose a heuristic algorithm to select the transmitting client and the packet combination that can reduce the decoding delay. Simulation results show that the proposed heuristic algorithm can achieve a tolerable degradation compared to the full SSU performance while using a smaller number of SSUs.

Instantly decodable network coding for real-time scalable video broadcast over wireless networks

In this paper, we study real-time scalable video broadcast over wireless networks using instantly decodable network coding (IDNC). Such real-time scalable videos have hard deadline and impose a decoding order on the video layers. We first derive the upper bound on the probability that the individual completion times of all receivers meet the deadline. Using this probability, we design two prioritized IDNC algorithms, namely the expanding window IDNC (EW-IDNC) algorithm and the non-overlapping window IDNC (NOW-IDNC) algorithm. These algorithms provide a high level of protection to the most important video layer, namely the base layer, before considering additional video layers, namely the enhancement layers, in coding decisions. Moreover, in these algorithms, we select an appropriate packet combination over a given number of video layers so that these video layers are decoded by the maximum number of receivers before the deadline. We formulate this packet selection problem as a two-stage maximal clique selection problem over an IDNC graph. Simulation results over a real scalable video sequence show that our proposed EW-IDNC and NOW-IDNC algorithms improve the received video quality compared to the existing IDNC algorithms.

Delivery time reduction for order-constrained applications using binary network codes

Consider a radio access network wherein a basestation is required to deliver a set of order-constrained messages to a set of users over independent erasure channels. This paper studies the delivery time reduction problem using instantly decodable network coding (IDNC). Motivated by time-critical and order-constrained applications, the delivery time is defined, at each transmission, as the number of undelivered messages. The delivery time minimization problem being computationally intractable, most of the existing literature on IDNC propose suboptimal online solutions. This paper suggests a novel method for solving the problem by introducing the delivery delay as a measure of distance to optimality. An expression characterizing the delivery time using the delivery delay is derived, allowing the approximation of the delivery time minimization problem by an optimization problem involving the delivery delay. The problem is, then, formulated as a maximum weight clique selection problem over the IDNC graph wherein the weight of each vertex reflects its corresponding user and messages delay. Simulation results suggest that the proposed solution achieves lower delivery and completion times as compared to the best-known heuristics for delivery time reduction.

Rate-aware network codes for completion time reduction in device-to-device communications

In this paper, we consider a fully connected device-to-device communications network, where a group of devices with heterogeneous channel capacities cooperate with each other to recover their missing packets. In such cooperative network, we aim to minimize the completion time required for recovering all missing packets at devices using instantly decodable network coding (IDNC). In particular, we first introduce a new IDNC graph to represent all feasible rate and coding decisions for all potential transmitting devices in one unified framework. We then show that finding the optimal schedule that minimizes the completion time is computationally complex. Nevertheless by using the new graph and the properties of the optimal schedule, we design a completion time reduction heuristic that balances between the transmission rate and the number of targeted devices with a new packet. Simulation results show that our proposed IDNC algorithm provides an appreciable completion time gain compared to the conventional rate oblivious network coding algorithms.

In order packet delivery in instantly decodable network coded systems over wireless broadcast

In this paper, we study in-order packet delivery in instantly decodable network coded systems for wireless broad- cast networks. We are interested in applications, in which the successful delivery of a packet depends on the correct reception of this packet and all its preceding packets. We formulate the problem of minimizing the number of undelivered packets to all receivers over all transmissions until completion as a stochastic shortest path (SSP) problem. Although finding the optimal packet selection policy using SSP is computationally complex, it allows us to draw guidelines for efficient packet selection policies. According to these guidelines, we design a simple heuristic packet selection algorithm. Simulation results illustrate that our proposed algorithm provides quicker packet delivery to the applications compared to the existing algorithms in the literature.

Network Coding for Video Distortion Reduction in Device-to-Device Communications

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.

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.

On Reducing Intercept Probability for Unsubscribed Video Layers Using Network Coding

In this letter, we use random linear network coding (RLNC) to deliver a two layered video sequence to two wireless subscribers such that a high-paying subscriber receives the highest quality and a low-paying subscriber receives the basic quality. We first derive closed-form expressions for the probability of the low-paying subscriber intercepting sufficient coded packets to experience the highest video quality. We consider both feedback-aided and feedback-free transmissions. We propose a resource allocation framework that minimizes the intercept probability subject to deadline and subscribed video quality constraints. We show the tightness between the theoretical and simulation results, and illustrate the protection achieved by RLNC for subscription-based layered video delivery.