隨著電腦與網路的普及，不同使用者間資料的分享也越來越頻繁，點對點(P2P)傳輸系統因此孕育而生，如Foxy、Bit Torrent、eMule等，打破以往主從式的傳輸架構，讓使用者能透過自己的電腦分享彼此的資料。然而網路傳輸中不可避免的會有資料遺失的情況，若使用重送機制對於網路系統會是一個相當大的負擔，因此使用錯誤更正碼的技術，希望我們提供一些額外的資料便可復原原來的資料。近年來已有許多針對錯誤更正碼(如RS-Code)在P2P系統上的研究，但這類有固定碼率的錯誤更正碼有其一定的更正能力，當超出這個更正能力的範圍時，資料便會大幅度的遺失造成整體系統效能低落。因此我們這裡引進一個近年較為熱門，名為噴泉碼的錯誤更正碼，其沒有碼率的概念因此沒有更正能力的問題。由於網路資源並不是隨時都是足夠的，當資源有限時勢必要做一些犧牲來提高整體系統的效能，因此我們利用噴泉碼在解碼時所需的冗餘資料之特性提出一個解碼率分佈，並根據此分佈所推測出來的解碼成功率提供給解碼率預測模型，可推測出某個節點在網路中因錯誤傳遞效應的實際解碼率；用上述之預測模型可以提供給一個上傳資源不足的節點做節點選擇的參考，以期在有限的時間內提高系統整體解碼率，進而提升整體系統效能。實驗結果顯示，該預測模型具有相當可靠的準確性，而使用該模型所做的節點選擇也確實提升了系統效能。

ECC (Error-Correcting Codes) is used to recover loss data due to error in network data transmission. The traditional ECC has fixed code rate, the code rate will affect the ability of error correction, if channel condition is so bad that the quantity of data loss is too large to recover, the overall system decoding performance will drop quickly due to error propagation in P2P streaming system. In the thesis, we use fountain code combine with P2P system, fountain code doesn’t have the fixed code rate, so there is no problems of error checking and correction. We proposed a decoding distribution which is an approximation to statistic results for fountain code, and we put the distribution’s information into our distributed decoding probability predicted model which considers error propagation. Furthermore, we can do peer selection by using the predicted decoding probability when the upload bandwidth is not enough. Our experimental results show that the proposed predicted model has high accuracy, and the overall system decoding probability also increases after our peer selection method.

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