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 modelling 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 to the existing IDNC algorithms.
The works in @cite_22 @cite_37 adopted IDNC for a partially connected D2D network and addressed the problem of servicing a large number of devices with any new packet in each time slot. However, these works are not readily compatible with the real-time video sequence that has a hard deadline and unequally important video packets. In contrast to @cite_22 @cite_37 , we introduce a novel IDNC graph that represents all feasible coding and transmission conflict-free decisions in one unified framework and develop an efficient IDNC framework that prioritizes the distribution of high importance video packets to all devices before the deadline.
@cite_37 @cite_22
1594194634 2089001139
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. We consider a group of n wireless clients and a set of k messages. Each client initially holds a subset of messages and is interested in an arbitrary subset of messages. Each client cooperates with other clients to obtain the set of messages it wants by exchanging instantly decodable network coded (IDNC) packets. This problem setting is known as the cooperative index coding problem. Clients are assumed to be connected through an arbitrary topology. In the absence of any known algorithm to complete the exchange of packets for general network topologies, we propose a greedy algorithm to satisfy the demands of all the clients with the aim of reducing the mean completion time. Our algorithm, in a completely distributed fashion, decides which subset of clients should transmit at each round of transmission and which messages should be coded together by each transmitting client to generate an IDNC packet. The algorithm encourages transmissions which are decodable for a larger number of clients and attempts to avoid collisions. We evaluate the performance of our algorithm via numerical experiments.