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研究生: 徐紹傑
Hsu, Shao-Chieh
論文名稱: 在5G蜂窩網路中基於可擴展視訊編碼(SVC)的多媒體群播廣播服務(MBS)的資源最小化分組法和效益導向(RMSG_U)式之資源配置方法
The Resource-Minimized Sub-Grouping and Utility-centric (RMSG_U) Resource Allocation Method for Scalable Video Coding (SVC) -based Multimedia's Multicast Broadcast Service (MBS) over the 5G Cellular Network
指導教授: 黃崇明
Huang, Chung-Ming
學位類別: 碩士
Master
系所名稱: 電機資訊學院 - 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年: 2024
畢業學年度: 112
語文別: 英文
論文頁數: 85
中文關鍵詞: 多播廣播服務(MBS)可擴展視頻編碼(SVC)點對多點(PTM)分組車輛用戶(VUs)通道質量指示器(CQI)
外文關鍵詞: Multicast Broadcast Services (MBS), Scalable video coding (SVC), Point-To-Multipoint (PTM), Grouping, Vehicular Users (VUs), Channel Quality Indicator (CQI)
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  • 這項工作提出了資源最小化子群組和效用為中心(RMSG_U)資源分配方法,以解決基於可擴展視頻編碼(SVC)的多媒體多點廣播服務(MBS)在5G蜂窩網絡上的資源分配問題。所提出的RMSG_U方法包含兩個階段。第一階段計算了多媒體多點廣播組的所有車載用戶(VUs)對每個可用的資源區塊(RB)感知的最小通道質量指標(CQI)值,該值可用於多媒體的MBS。然後,選擇最小數量的RB,每個RB根據前述最小CQI值採用調製和編碼方案(MCS)值,以傳輸對應的多媒體多點廣播組的訂閱多媒體服務的基礎層(BL)。通過這種方式,對應組的所有VUs都可以接收訂閱多媒體服務的BL。在第二階段,假設當前有n個可用的RB,即RB1,RB2,..,RBn,對於多媒體多點廣播組的所有VUs對於RBi,i=1..n,的最小感知CQI值為CQIi,i=1..n,分別為RBi,RMSG_U使用最小的k個RBs, 其中(i)這k個RB的CQI值是這n個CQIi中的前k個,且(ii)所有這k個RB都採用CQIB, B∈{1,2,..,n}中最大的MCS值,即所有CQIi,i=1..n,使得這k個RB的傳輸數據速率等於或大於要傳輸的多媒體多點廣播服務相應EL的視頻數據速率。通過這種方式,這k個RB的感知CQI值都大於CQIB的這個多媒體多點廣播服務的VUs可以成為可以接收相應EL的VUs子組,結果相同子組的VUs可以使用最小數量的RB來傳輸相應的EL,這可能為傳輸更多多媒體多點廣播服務的EL提供更多的剩餘RB。根據性能評估,所提出的RMSG_U方法(1)通常具有更好的頻譜效率和公平性,(2)在VUs的CQI值非常分散的情況下,具有更好的平均視頻速率並且需要較少的RB數量,相對於其他方法。

    This work proposes the Resource-Minimized Sub-Grouping and Utility-centric (RMSG_U) resource allocation method to resolve the resource allocation problem for Scalable video coding (SVC)-based multimedia’s Multicast Broadcast Service (MBS) over the 5G cellular network. The proposed RMSG_U method contains two stages. The 1st stage calculates the minimum Channel Quality Indicator (CQI) value perceived by all Vehicular Users (VUs) of a multimedia multicast group for each available Resource Block (RB) that can be used for multimedia’s MBS. Then, selecting the minimum number of RBs, for which each RB adopts the Modulation and Coding Scheme (MCS) value based on the aforementioned minimum CQI value, to transmit the corresponding multimedia multicast group’s Base Layer (BL) of the subscribed multimedia service. In this way, all of VUs in the corresponding group can receive the BL of the subscribed multimedia service. In the 2nd stage, let there be n available RBs, i.e., RB1,RB2,..,RBn, currently and the minimum perceived CQI value of all VUs of a multimedia multicast group for RBi, i=1..n, be CQIi, i=1..n, respectively; RMSG_U uses the minimum k RBs (i) whose CQI values are the top k among these n CQIi, i=1..n, and (ii) all of these k RBs adopt the MCS corresponding to CQIB, B ∈ {1,2,..,n}, that is the biggest among all CQIi, i=1..n, such that the transmitted data rate of these k RBs is equal to or greater than the multimedia multicast service’s corresponding EL’s video data rate to be transmitted. In this way, the considered multimedia multicast service’s VUs whose perceived CQI values for these k RBs are all bigger than CQIB can be the subgroup of VUs that can receive the corresponding EL. As a result, those VUs of the same subgroup can use the minimum number of RBs to transmit the corresponding EL, which potentially results in more remaining RBs for transmitting more multimedia multicast services’ ELs. According to the performance evaluation, the proposed RMSG_U method (i) has the better spectral efficiency and fairness generally and (2) has better average video rate and needs the smaller number of RBs in the situation of VUs having very disperse CQI values comparing with other methods.

    摘要 i Abstract ii 誌謝 iv Contents v List of Figures vii List of Tables viii Chapter 1 Introduction 1 Chapter 2 Related Works 10 Chapter 3 Preliminaries 15 3-1. CQI-MCS Mapping 15 3-2. Resource Constraints 16 3-3. Layer Constraints 18 3-4. Fundamentals of the Resource Allocation for MBS 18 Chapter 4 The Functional Scenario of RMSG_U 21 4-1. The Principle of Resource Allocation for BL 21 4-2. The Principle of Resource Allocation for EL 22 Chapter 5 RMSG_U Resource Allocation Algorithms 24 5-1. BL's Resource Allocation with Grouping in Details 24 5-2. EL's Resource Allocation with Grouping in Details 28 5-3. EL's Resource Allocation without Grouping 33 Chapter 6 The Computation Complexity of the Proposed Algorithms 39 6-1. Computation Complexity of Algorithm 1 39 6-2. Computation Complexity of Algorithm 2 40 6-3. Computation Complexity of Algorithm 3 41 6-4. Comparison of the Computation Complexity between Algorithm 2 and Algorithm 3 42 Chapter 7 Performance Analysis 43 7-1. The Simulation Environment 43 7-2. Performance Metrics 44 7-3. The Compared Methods 47 7-4. Performance Evaluation Results 50 7-5. Comparison of the Computation Complexity among RMSG_U, MSML and MQS 65 Chapter 8 Conclusion 66 Bibliography 68

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