Viewing experience optimization for peer-to-peer streaming networks with credit-based incentive mechanisms

Effective in fighting against “free-riding” and stimulating the cooperation between peers, credit-based incentive mechanisms are widely adopted in today's peer-to-peer (P2P) streaming networks. This work considers a P2P multimedia streaming system that relies on credits for incentivizing peers to upload. The main problem of focus is to derive the optimal strategy for a peer, in terms of allocating its credits across different time slots, to maximize its long-term viewing experience. Especially, the dynamic changing feature of credits is taken into consideration when we formulate the problem, and the optimal credits allocation is shown to be a staircase-like function over time. Then, based on the characteristics of the optimal credits allocation strategy, an effective double-loop iterative algorithm is proposed. For the consideration of practical implementation, three low-complexity credits allocation strategies are proposed. It is shown that each of the strategies has its own feature and is suitable for a specific scenario. Then, as an extension, the proposed credits allocation schemes are reinvestigated for P2P streaming networks that adopt dynamic-pricing credits-based incentive mechanisms. It is shown that the previously obtained credits allocation strategies and algorithms can be easily applied to these systems with minor modifications.