This　paper　proposes　a　novel　networked　iterative　learning　control　(NILC)　scheme　with　adjustment　factor　for　a　class　of　discrete-time　uncertain　nonlinear　systems　with　stochastic　input　and　output　packet　dropout　modeled　as　0-1　Bernoulli-type　random　variable.　Firstly,　the　equivalence　relation　between　the　realizability　of　controlled　system　and　the　input-output　coupling　parameter　(IOCP)　is　established.　Secondly,　in　order　to　overcome　the　main　obstacle　arising　from　the　unknown　IOCP,　an　identification　technique　is　developed　for　it.　Thirdly,　it　is　strictly　proved　that,　under　certain　conditions,　the　tracking　errors　driven　by　the　developed　NILC　scheme　are　convergent　to　zero　along　iteration　direction　in　the　sense　of　expectation.　Finally,　an　example　is　given　to　demonstrate　the　effectiveness　of　the　proposed　NILC　scheme　and　the　merits　of　adjustment　factor.