A Polynomial-Time Algorithm for Pliable Index Coding

Linqi Song, Christina Fragouli

Abstract: In pliable index coding, we consider a server withm messages and n clients, where each client has as side information a subset of the messages. We seek to minimize the number of broadcast transmissions, so that each client can recover any one unknown message she does not already have. Previous work has shown that the pliable index coding problem is NP-hard and requires at most O(log <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (n)) broadcast transmissions, which indicates exponential savings over the conventional index coding that requires in the worst case O(n) transmissions. In this paper, building on a decoding criterion that we propose, we first design a deterministic polynomial-time algorithm that can realize the exponential benefits, by achieving, in the worst case, a performance upper bounded by O(log <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (n)) broadcast transmissions. We extend our algorithm to the t-requests case, where each client requires t unknown messages that she does not have, and show that our algorithm requires at most O(t log(n) + log <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (n)) broadcast transmissions. We construct lower bound instances that require at least Ω(log(n)) transmissions for linear pliable index coding and at least Ω(t + log(n)) transmissions for the t-requests case, indicating that both our upper and lower bounds are polynomials of log(n) and differ within a factor of O(log(n)). We provide a probabilistic analysis over random instances and show that the required number of transmissions is almost surely Θ(log(n)), as compared with the Θ(n/ log(n)) for index coding. In addition, we show that these upper and lower bounds also hold for vector pliable index coding in the worst case instances and the random graph instances, implying that vector coding does not provide benefits in terms of these bounds. Our numerical experiments show that our algorithm outperforms existing algorithms for pliable index coding by up to 50% less transmissions.