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研究生: 吳思緯
Wu, Ssu-Wei
論文名稱: 於火車情境下運用巨量資料之適地性視訊多媒體串流控制機制
The Location-Based Video Streaming Control Scheme for Trains using the Big Data Mechanism
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: 2014
畢業學年度: 102
語文別: 英文
論文頁數: 38
中文關鍵詞: 視訊串流適地性服務 (Location-Based Service)可適性傳送率控制資料預載(Pre-Buffering)
外文關鍵詞: streaming, location-based service, adaptive rate control, pre-buffering
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  • 於行動網路上的視訊串流常受到訊號衰減與屏蔽效應的影響。舉例而言,當一輛火車行駛在路線固定的軌道上,訊號將隨著移動路徑而改變,特別是在隧道或地下道等特定地點中。這種劇烈的訊號變化將導致封包的遺失並影響到影片播放品質,若隧道的長度過長,更可能導致行動裝置端串流緩衝資料量(streaming buffer)的不足。因此,我們提出了一個於火車情境下的適地性視訊串流控制機制(Location-Based Video Streaming Control Scheme for Trains)。基於火車具行駛於固定路線等特性,訊號狀況的變化將會呈現特定的模式。我們所提出的LBVS-T機制,可以根據地形環境變化所導致的網路連線中斷情形,調整資料的傳送速率。從效能測試結果可看出,在火車接近隧道、行駛於隧道中以及離開隧道的各個期間,我們所提出的機制可改善串流緩衝資料量用盡的狀況,並且提供較為平順的串流品質。

    Video streaming over the wireless mobile network suffers from signal fading and shadowing effects. For instance, when the train is moving in a fixed route over the railway, the signal will vary along the moving route, especially on some specific positions, e.g., tunnels and underground. This serious signal variation results in packets loss and affects the video presentation quality. Furthermore, if the length of the tunnel is too long, the streaming buffer at the client side will have the starvation phenomenon. In this paper, we proposed a video streaming control mechanism called Location-Based Video Streaming Control mechanism for Trains (LBVS-T). Since the characteristics of the train are moving on a fixed route, the signal variation will show a specific pattern. The proposed mechanism can adjust the transmission rate based on the network disruption situation, which is caused by the geographic environment. The performance results demonstrate that, LBVS-T can tackle the buffer starvation problem and have more smooth streaming performance when a train is moving before entering into, inside and just outside tunnels.

    摘要 I Abstract II 誌謝 III Contents IV List of Figures V List of Tables VI Chapter 1 Introduction 1 Chapter 2 Related Works 4 2.1 The Location-Based Network Prediction 4 2.2 Big Data in the Transportation System 5 2.3 Wireless Video Streaming 6 Chapter 3 Problem Statement 8 3.1 Approaching tunnel stage 9 3.2 Signal fluctuating stage after entering into the tunnel 10 3.3 Network disconnection stage 10 3.4 Signal fluctuating stage before leaving the tunnel stage 11 Chapter 4 The Buffering Situation of the Moving Train 12 Chapter 5 Derivation of the Network’s Situation 15 Chapter 6 Streaming Control Schemes 19 6.1 The In-Sequence Transmission (IST) Scheme 19 6.2 The Concurrent and Rate-Distributed Transmission (CRDT) Scheme 20 6.3 Concurrent and Remaining-Bandwidth-Utilized Transmission (CRBUT) Scheme 22 Chapter 7 Performance Analysis 23 7.1 The network situations for performance analysis 23 7.2 Simulation of proposed mechanism without background traffic 26 7.3 Simulation of proposed mechanism with background traffic 30 Chapter 8 Conclusions 35 Bibliography 36

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