點對點視訊傳輸系統在現今的網際網路中已相當普及，使用者自願性的提供頻寬以利系統架構出點對點傳輸系統。然而，自私使用者只下載卻不上傳任何檔案的行為導致其餘使用者的視訊品質不穩定甚至讓點對點系統崩解。面對這類型的問題，現存的研究方法利用過去在點對點檔案分享系統上成功的獎勵機制，移轉至點對點視訊串流傳輸系統架構上，這些方法的特色是採用周圍鄰近使用者對我的貢獻程度，作為我分配資源的依據條件。但是並非所有類型的點對點視訊傳輸系統都適用於這種主觀依據自己得到的好處來分配資源的獎勵機制。推拉式的點對點視訊傳輸架構中，使用者互相提供頻寬資源的狀況並非常見的傳輸狀態，現有的獎勵方法利用相互間的貢獻度分配資源在推拉式的點對點傳輸架構下造成使用者壁壘分明無法互相傳輸視訊封包的窘況。我們利用信譽評等的方式在推拉式的點對點視訊傳輸架構下建立獎勵機制，使用者替自己以外的鄰近使用者評分，累積這些分數得到每個使用者對點對點視訊傳輸系統的整體貢獻度，系統內每個使用者都可以得到這項資訊做為是否同意其他使用者不同視訊層級傳輸要求的依據，根據我們的機制可以讓不同貢獻度的使用者有不同的視訊品質並壓抑自私使用者的視訊品質至相當低的水平。

Using Peer-to-Peer (P2P) media streaming service can be observed everywhere in the Internet nowadays. However, free-riders that down without providing leads to unstable video quality of peers in the system. Current research of incentive methods in P2P media streaming system modifies the original incentive methods in the P2P file sharing systems. Each peer allocates resource according to the amount of receiving data from peers. These methods are successful in some kinds of media streaming systems but not all of these systems. We use the reputation-based method for building an incentive mechanism in the push-pull based media streaming system. Peers rate to others peers and use the overall system contribution of each peer for making transmission decision. In our incentive mechanism, peers receive different level of video quality according to their contribution of the systems. And free-riders can only receive such a lowest level of video quality.

Abstract i
摘 要 ii
Content iii
Chapter 1 Introduction 1
Chapter 2 Related Work 5
2.1 Peer-to-Peer Media Streaming Structure 5
2.1.1 Tree-Based Structure 6
2.1.2 Mesh-Pull Based Structure 7
2.1.3 Push-Pull Based Structure 8
2.2 Scalable Video Coding (SVC) 9
2.3 Problem Discussion 10
Chapter 3 Proposed Incentive Mechanism 14
3.1 Reputation System 15
3.1.1 Score Map 18
3.1.2 Contribution Map 19
3.1.3 Decentralized Mechanism 21
3.2 Allowing Level Function 23
3.3 Quality Estimation 25
3.4 Misbehavior Prevention 26
3.4.1 Free-rider 27
3.4.2 Cheat-Proof 28
Chapter 4 Simulation Results 33
4.1 Simulation Scenario 33
4.2 Compare with Game-Theoretic Method 36
4.3 Different Percentages of Free-riders 40
Chapter 5 Conclusion 43

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