  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.
