  In this paper, we consider the problem of minimizing the multicast decoding
delay of generalized instantly decodable network coding (G-IDNC) over
persistent forward and feedback erasure channels with feedback intermittence.
In such an environment, the sender does not always receive acknowledgement from
the receivers after each transmission. Moreover, both the forward and feedback
channels are subject to persistent erasures, which can be modelled by a two
state (good and bad states) Markov chain known as Gilbert-Elliott channel
(GEC). Due to such feedback imperfections, the sender is unable to determine
subsequent instantly decodable packets combination for all receivers. Given
this harsh channel and feedback model, we first derive expressions for the
probability distributions of decoding delay increments and then employ these
expressions in formulating the minimum decoding problem in such environment as
a maximum weight clique problem in the G-IDNC graph. We also show that the
problem formulations in simpler channel and feedback models are special cases
of our generalized formulation. Since this problem is NP-hard, we design a
greedy algorithm to solve it and compare it to blind approaches proposed in
literature. Through extensive simulations, our adaptive algorithm is shown to
outperform the blind approaches in all situations and to achieve significant
improvement in the decoding delay, especially when the channel is highly
persistent
