ABSTRACT
Contents 19.1 Introduction and Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 430 19.2 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 432 19.3 Congestion in P2P Streaming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435 19.4 Optimal P2P Content Exchange . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 437 19.5 Equilibrium P2P Content Exchange . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441
19.5.1 Unconstrained Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441 19.5.2 Constrained Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 444
19.6 Simulation Design and Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 448 19.6.1 Unconstrained Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 449 19.6.2 Constrained Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 451
19.7 Open Issues and Developments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 452 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 455
This work mainly proposes to model real-time streaming in congestion game form: at each stage, given the prevailing content distribution, peers (or players) choose who to ask for an additional content unit. In this way, (stage-and peer-wise) congestion is measured precisely by counting how many requests to forward are addressed to each peer (and to the broadcaster). While in the wired setting, all nodes (or peers, together with the broadcaster) are assumed to directly communicate with
each other, in the wireless framework, an ad hoc network formalizes transmission constraints due to spatial positions: the broadcaster and each peer can send content only to those at unitary hop distance from them in such a network. For the wired (or fully connected) setting, we develop a strategy restriction mechanism whose equilibrium outcomes minimize both streaming length (or number of stages needed to spread the whole content over the whole population) and congestion, while also fulfilling a fairness condition. As this mechanism heavily relies upon connectivity among nodes, in general it does not apply to the wireless (or non-fully connected) case. Accordingly, the focus next turns on equilibrium outcomes with no strategy restriction apart from communication constraints due to the ad hoc network. In the wired setting, simulations allow to compare equilibrium outcomes with and without the proposed strategy restriction mechanism, while in the wireless one, they are used for evaluating how performance deteriorates, at equilibrium, as the size of the given ad hoc network decreases.
