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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.

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.

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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.