Strategyproof Mechanisms for Content Delivery via Layered Multicast

Layered multicast exploits the heterogeneity of user capacities, making it ideal for delivering content such as media streams over the Internet. In order to maximize either its revenue or the total utility of users, content providers employing layered multicast need to carefully choose a routing, layer allocation and pricing scheme. We study algorithms and mechanisms for achieving either goal from a theoretical perspective. When the goal is maximizing social welfare, we prove that the problem is NP-hard, and provide a simple 3-approximation algorithm. We next tailor a payment scheme based on the idea of critical bids to derive a truthful mechanism that achieves a constant fraction of the optimal social welfare. When the goal is revenue maximization, we first design an algorithm that computes the revenue-maximizing layer pricing scheme, assuming truthful valuation reports. This algorithm, coupled with a new revenue extraction procedure for layered multicast, is used to design a randomized, strategyproof auction that elicits truthful reports. Employing discrete martingales to model the auction, we show that a constant fraction of the optimal revenue can be guaranteed with high probability. Finally, we study the efficacy of our algorithms via simulations.

[1]  Rudolf Ahlswede,et al.  Network information flow , 2000, IEEE Trans. Inf. Theory.

[2]  Mohammad R. Salavatipour,et al.  Packing Steiner trees , 2003, SODA '03.

[3]  Zongpeng Li,et al.  Efficient and distributed computation of maximum multicast rates , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[4]  Steven McCanne,et al.  Receiver-driven layered multicast , 2001 .

[5]  Kazuoki Azuma WEIGHTED SUMS OF CERTAIN DEPENDENT RANDOM VARIABLES , 1967 .

[6]  Roger B. Myerson,et al.  Optimal Auction Design , 1981, Math. Oper. Res..

[7]  Zizhuo Wang,et al.  A unified framework for dynamic pari-mutuel information market design , 2009, EC '09.

[8]  E. H. Clarke Multipart pricing of public goods , 1971 .

[9]  William Vickrey,et al.  Counterspeculation, Auctions, And Competitive Sealed Tenders , 1961 .

[10]  Theodore Groves,et al.  Incentives in Teams , 1973 .

[11]  Anna R. Karlin,et al.  Competitive auctions , 2006, Games Econ. Behav..

[12]  Tim Roughgarden,et al.  Algorithmic Game Theory , 2007 .

[13]  K. Rijkse,et al.  H.263: video coding for low-bit-rate communication , 1996, IEEE Commun. Mag..

[14]  Zongpeng Li,et al.  On achieving optimal throughput with network coding , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[15]  Zongpeng Li,et al.  Optimal layered multicast , 2011, TOMCCAP.

[16]  Gagan Ghosh Multi-unit auctions with budget-constrained bidders , 2012 .

[17]  Noam Nisan,et al.  Algorithmic mechanism design (extended abstract) , 1999, STOC '99.

[18]  Martin Thimm,et al.  On the approximability of the Steiner tree problem , 2003, Theor. Comput. Sci..

[19]  Keith W. Ross,et al.  Computer networking - a top-down approach featuring the internet , 2000 .

[20]  Jin Zhao,et al.  LION: Layered Overlay Multicast With Network Coding , 2006, IEEE Transactions on Multimedia.

[21]  Peter Bro Miltersen,et al.  On Pseudorandom Generators in NC , 2001, MFCS.