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研究生: 高健淇
Kao, Chien-Chi
論文名稱: 在具備睡眠模式的OFDMA網路中針對影像串流群播之排程演算法
Efficient Scheduling for Video Multicast over Sleep Mode Enabled OFDMA Networks
指導教授: 楊舜仁
口試委員: 楊舜仁
林一平
林風
廖婉君
陳志成
高榮駿
許健平
學位類別: 博士
Doctor
系所名稱: 電機資訊學院 - 資訊工程學系
Computer Science
論文出版年: 2014
畢業學年度: 103
語文別: 英文
論文頁數: 79
中文關鍵詞: 群播正交分頻多重存取可調式影像串流排程演算法睡眠模式
外文關鍵詞: Multicast, OFDMA, scalable video, scheduling algorithm, sleep mode
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  • 在OFDMA網路中,針對影像串流群播這類多媒體應用服務,資源排程一直是具有挑戰性的研究議題。在OFDMA的訊框架構下,排程工作包括(1)訊框內排程和(2)跨訊框排程。訊框內排程負責決定一個OFDMA訊框內的頻寬分配,而跨訊框排程則負責決定在一連串OFDMA訊框之間的資料傳輸順序。在過去的文獻中,雖已有許多研究致力於訊框內排程機制的發展,卻少有研究著眼在跨訊框排程機制。此篇論文主要研究在具備睡眠模式的OFDMA網路中,如何運用跨訊框排程機制以支援
    可調式影像串流群播服務。在本論文中,我們提出兩種高效率的跨訊框排程演算法。第一,我們提出一個睡眠時間交錯演算法。此演算法適當地調節一個睡眠模式參數,讓行動使用者在執行睡眠模式的同時,能夠有效地保證其影像群播服務的頻寬使用效率。第二,我們提出一個多重裝箱演算法。利用分而治之的策略,此演算法能夠有效地排除不必要的清醒期間以及不必要的狀態轉換(在清醒與睡眠之),進而達成高節能效率。針對我們提出的兩種排程演算法,在時間複雜度、電量使用效率、封包傳送成功率和使用者滿意度等方面,本論文會以理論分析與實驗模擬結果表明其效能。


    Resource scheduling remains a challenging issue in OFDMA networks, especially for multimedia applications such as video multicast. Given the OFDMA frame structure, the scheduling task is comprised of 1) intraframe scheduling and 2) interframe scheduling. Intraframe scheduling determines the bandwidth allocation within an OFDMA frame, whereas interframe scheduling determines the data transmission order for a series of OFDMA frames. In the literature, while many studies concentrated on the development of intraframe scheduling mechanisms, few studies looked at the potential of interframe scheduling mechanisms. This dissertation investigates the potential of interframe scheduling algorithms to support scalable-video multicast over sleep mode enabled OFDMA networks. In this dissertation, we present two efficient interframe scheduling algorithms, described as follows. First, we propose a sleep-mode interleaving algorithm. By appropriately adjusting one sleep mode parameter, the proposed interleaving algorithm effectively guarantees the bandwidth efficiency of the video multicast mechanisms while mobile users can execute the sleep mode operations. Second, we propose a multiple bin-packing algorithm (MBPA). By applying the divide-and-conquer strategy, MBPA effectively eliminates unnecessary wake-up periods and unnecessary state transitions (between wake-up and sleep states), and thus achieves high energy-efficiency. Theoretical analysis and simulation results show the effectiveness of the proposed scheduling algorithms in computational complexity, energy efficiency, packet delivery ratio, and user satisfaction.

    Acknowledgements . . . i Abstract . . . ii Contents . . . ii List of Figures . . . vi List of Tables . . . viii 1 Introduction . . . 1 2 Background . . . 6 3 Sleep-Mode Interleaving Algorithm . . . 8 3.1 Problem Formulation . . . 8 3.1.1 Model and Notations . . . 8 3.1.2 Assumptions . . . 11 3.1.3 Constraint and Goals . . . 11 3.1.4 Problem Statement . . . 13 3.1.5 Relaxing Assumption I: Size of Multicast Groups . . . 17 3.1.6 Relaxing Assumption II: Slot Consumption of Data Units . . . 18 3.2 Sleep-Mode Interleaving Algorithm . . . 19 3.2.1 Concept of the Sleep-Mode Interleaving Algorithm . . . 19 3.2.2 Intelligent Method for Data/User Counting Window Construction . . . 20 3.2.3 Advanced Method for Slot Counting Window Construction . . . 21 3.2.4 Two-Phase Sleep-Mode Interleaving Algorithm: Brute Force Method . . . 24 3.2.5 Two-Phase Sleep-Mode Interleaving Algorithm: Greedy Method . . . 24 3.3 Analysis of Computational Complexity . . . 27 3.3.1 Intelligent Method for Data/User Counting Window Construction . . . 27 3.3.2 Advanced Method for Slot Counting Window Construction . . . 27 3.3.3 Brute Force Method for Sleep-Mode Interleaving Algorithm . . . 27 3.3.4 Greedy Method for Sleep-Mode Interleaving Algorithm . . . 28 3.3.5 Computational Cost of Re-execution . . . 28 3.4 Performance Evaluation . . . 29 3.4.1 Simulation Environment . . . 29 3.4.2 Input Parameters and Output Measures . . . 30 3.4.3 Comparison between Brute Force Method and Greedy Method . . . 31 3.4.4 First Phase Performance: Using Data Counting Window . . . 33 3.4.5 First Phase Performance: Using Slot Counting Window . . . 34 3.4.6 Second Phase Performance: Using User Counting Window . . . 36 4 Multiple Bin-Packing Algorithm . . . 38 4.1 Problem Formulation . . . 38 4.1.1 System Model . . . 38 4.1.2 Goal and Constraints . . . 39 4.1.3 Problem Hardness . . . 41 4.2 Multiple Bin-Packing Algorithm . . . 43 4.2.1 Concept of the Proposed Multiple Bin-Packing Algorithm . . . 43 4.2.2 The Proposed Multiple Bin-Packing Algorithm . . . 45 4.2.3 Procedure of the Proposed Multiple Bin-Packing Algorithm . . . 49 4.2.4 Difference between Multiple Bin Packing Algorithm and Pure Bin Packing Algorithm . . . 52 4.2.5 Compatibility with Intraframe Scheduling Algorithms . . . 53 4.3 Theoretical Analysis . . . 55 4.3.1 Time Complexity . . . 55 4.3.2 Performance Bound . . . 56 4.4 Simulation Results . . . 62 4.4.1 Simulation Environment . . . 62 4.4.2 Comparison with Optimal Algorithm . . . 64 4.4.3 Comparison with Other Algorithms: Energy Efficiency . . . 65 4.4.4 Comparison with Other Algorithms: QoE-Related Metrics . . . 67 5 RelatedWork . . . 69 6 Conclusion . . . 72 Bibliography . . . 74 Appendix . . . 78 A Publications . . . 78

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