We introduce a game theoretic framework for studying a restricted form 
of network coding in a general wireless network. The network is fixed 
and known, and the system performance is measured as the number of wireless 
transmissions required to meet n unicast demands. Game theory is here 
employed as a tool for improving distributed network coding solutions. 
The approach involves designing cost functions for individual "players" 
(unicast sessions) so that players independently pursuing their own 
selfish interests (i.e., minimizing their individual costs) achieve a 
desirable system performance in a shared network environment. We propose 
a family of cost functions and compare the performance of the resulting 
distributed algorithms to the best performance that could be found 
and implemented using a centralized controller. We focus on the 
performance of stable solutions -- where stability here refers to a 
form of Nash equilibrium. Results include bounds on the best- 
and worst-case stable solutions as compared to the optimal centralized 
solution. We show that our bound on the worstcase stable performance 
cannot be improved using cost functions that are independent of the network
structure. Results in learning in games prove that the bbest-case stable 
solution can be learned by self-interested players with probability 
approaching 1.