臺灣博碩士論文加值系統

這篇論文主要是在IEEE 802.11e 標準上，提出一個封包對應佇列演算法改善影像傳輸的品質與延遲。IEEE 802.11是現今最受歡迎的協定預設是利用分散式協調功能(DCF)機制去競爭通道，可是該機制中對於資料流並還沒有優先權的概念。在2005年，IEEE 802.11e 被訂定且該協定中定義了進階分散通道存取(EDCA)的機制來提供服務品質(QoS)。在IEEE 802.11e中，所有的視頻資料預設分配到同個存取類別(AC)，可是在重度負載網路環境下這樣的分配機制會導致影像傳輸品質下降。先前有不少研究提出了相關的封包對應佇列機制去提升整體影像傳輸的品質，但這些機制所提出的觀點並沒有真正考慮到影響封包離開佇列時間的關鍵因素。所以在此論文中，我們提出一個新的對應演算法，在此演算法中，我們考慮到影響封包離開佇列的兩個關鍵因素，分別是佇列的長度與佇列的吞吐量，藉由這樣的設計使視頻資料能夠盡快地被傳送而提升整體影像傳輸的品質。

In this thesis, we proposed an adaptive queue mapping mechanism to improve the quality and delay of video over the IEEE 802.11e. The IEEE 802.11 is the most popular protocol all over the world. In the IEEE 802.11, the default mechanism to access medium is called Distributed Coordination Function (DCF). But there is no notion of priority traffic in DCF mechanism. In 2005, the IEEE 802.11e was specified and defined Enhanced Distributed Channel Access (EDCA) to provide the Quality of Service (QoS). In the IEEE 802.11e, all video data are allocated to the same Access Category (AC), but it will cause the quality and delay of video to be downgraded in some network condition. There are several queue mapping mechanisms proposed to improve the video transmission, but these mechanisms do not consider the key factor that how fast packets can be de-queue. In this research, a queue mapping algorithm, Throughput-based Mapping Algorithm (TAM) is proposed to improve the quality and delay of video. In TMA, we consider two important factors which influence the speed of packets de-queue, and let video packets can be transmitted as soon as possible.

Content
Chapter 1 : Introduction 1

Chapter 2 : Background and Related Works 4
2.1 MPEG-4 Overview 4
2.2 IEEE 802.11-DCF Operation 6
2.3 IEEE 802.11e-Enhanced Distributed Channel Access 8
2.4 Related Works 11
2.4.1 Static mapping mechanism 11
2.4.2 Dynamic mapping mechanism 13

Chapter 3 : Throughput-based Mapping Algorithm 15
3.1 Throughput-based Mapping Algorithm 15
3.2 Throughput Analysis 16

Chapter 4 : Simulation Results 23
4.1 Environment Setting 23
4.2 Result of First Scenario 25
4.3 Result of Second Scenario 28

Chapter 5 : Conclusions and Future Work 34
5.1 Conclusions 34
5.2 Future Work 34

Reference 35

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[12]NS-2 simulator, http://csie.nqu.edu.tw/smallko/ns2/ns2.htm.

[13]EvalVid, http://www2.tkn.tu-berlin.de/research/evalvid/fw.html.

[14]YUV Video Sequence (QCIF),
http://www2.tkn.tu-berlin.de/research/evalvid/qcif.html .