網路通信技術進步與智慧型行動裝置普遍應用，多媒體串流成為主要的資訊傳遞媒體之一。MPEG於2011年提出可在傳輸安全協議 (HTTP) 上進行動態與自適性媒體串流的標準MPEG-DASH (Dynamic Adaptive Streaming over HTTP) ，當網路頻寬發生變化時可以適當調整視訊編碼參數，以提供使用者良好的收看品質。系統實作皆以單一伺服器架構為普遍，針對多伺服器的討論較少，而多伺服器架構中網路頻寬變動較為頻繁與不可預測，因此用戶端選擇串流來源時容易出現不穩定的狀況，然而相較於單伺服器串流系統可以提供較佳的頻寬利用率和多來源下載，能提供較有效率的視訊串流系統。本論文實作一個在多伺服器架構下的MPEG-DASH系統，所建構之系統能提供用戶更佳的視訊品質與播放順暢度，其中必須處理多伺服器間頻寬的運用、估測各伺服器間的網路頻寬狀態、依據網路優先順序進行下載排程、並且考慮當前暫存器狀態選擇當前系統最適當的視訊品質。另一部份是讓多用戶在多伺服器間可以穩定的接收視訊串流服務，用戶端選擇伺服器下載影片時會有搶資源的情況發生，我們將各下載片段排名，優先的片段可以先下載，而較低順位的片段則選擇等待或是找尋另外路徑下載，充分利用多伺服器的優勢，達到順暢且穩定的視訊串流服務。實驗結果顯示本論文提出之系統與方法，可以不受各伺服器間頻繁的頻寬變動與各用戶情況下的影響，且系統在平均下載位元率、平均品質層、品質變動率小、以及系統穩定度的表現上相較於參考方法都有較好的效果。

Multimedia streaming become one of the major information media with the help of network technology and widespread applications of smart personal devices. The MPEG-DASH (Dynamic Adaptive Streaming over HTTP) proposed in 2011 can help to provide stable perception quality under unstable network environments. For system implementation, it usually adopts one server for simplicity. For a multi-server streaming system to serve multi-users, it enables more flexible streaming scheduling but has to deal more system parameters. In this thesis, we implement a MPEG-DASH compatible multi-server streaming system, which can provide stable perception quality for users. The system has to deal with available bandwidth estimation, source selection for streaming, video buffer fullness, segment transmission scheduling problems to provide bandwidth compatible best perception quality. At the end device, one user can select which source to download the desired video segments. We proposed to set priority for video segments, such that it will download high priority segment first. Experiments showed that the proposed MPEG-DASH compatible multi-server video streaming system and method can provide more stable streaming services under unstable network environments. Performances on average of transmitted bitrates, average quality level, and level switching frequency are better than the previous work.

[1] Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2015–2020 White Paper (http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/mobile-white-paper-c11-520862.html).
[2] Transcoding - Wikipedia (https://en.wikipedia.org/wiki/Transcoding).
[3] Cloud computing - Wikipedia (https://en.wikipedia.org/wiki/Cloud_computing).
[4] 讓行動影音串流更順暢 MPEG-DASH新標準崛起(http://www.mem.com.tw/article_content.asp?sn=1207190001).
[5] HTTP Live Streaming Overview (https://developer.apple.com/library/mac/documentation/NetworkingInternet/Conceptual/StreamingMediaGuide/Introduction/Introduction.html).
[6] Microsoft Smooth Streaming (https://www.microsoft.com/silverlight/smoothstreaming).
[7] Adobe HTTP Dynamic Streaming (http://www.adobe.com/tw/products/hds-dynamic-streaming.html).
[8] ISO/IEC IS 23009-1: “Information technology - Dynamic adaptive streaming over HTTP (DASH) – Part 1: Media presentation description and segment formats, ” 2012.
[9] T. Wiegand. “Overview of the H.264/AVC video coding standard,” IEEE Trans. Circuits Systems Video Techn., vol. 13, no. 7, pp. 560-576, Jul. 2003.
[10] G. J. Sullivan and T. Wiegand. “Video compression-from concepts to the H.264/AVC standard,” in Proc. of the IEEE, vol. 93, no. 1, pp. 18-31, Jan. 2005.
[11] D. Marpe, T. Wiegard, and G. J. Sullivan. “The H.264/MPEG4 advanced video coding standard and its applications,” IEEE Commun. Mag., vol. 44, no. 8, pp. 134-143, Aug. 2006.
[12] T. Wiegard and G. J. Sullivan. “The H.264/AVC video coding standard,” IEEE Signal Process. Mag., vol. 24, no. 2, pp. 148-153, Mar. 2007.
[13] The Moving Picture Experts Group (http://mpeg.chiariglione.org).
[14] International Telecommunication Union (http://www.itu.int/en/pages/default.aspx).

[15] J. Xin, C.-W. Lin, and M.-T. Sun. “Digital Video Transcoding,” in Proc. of the IEEE, vol. 93, no. 1, pp. 84-97, Jan. 2005.
[16] M. Jain and C. Dovrolis. “Pathload: A measurement tool for end-to-end available bandwidth,” In Proc. of Passive and Active Measurements (PAM) Workshop, Mar. 2002.
[17] M. Jain and C. Dovrolis. “End-to-End available bandwidth: measurement methodology, dynamics, and relation with TCP Throughput,” IEEE/ACM Trans. on Networking, vol. 11, no. 4, pp. 537-549, Aug. 2003.
[18] S. Akhshabi, L. Anantakrishnan, C. Dovrolis, and A. C. Begen. “What happens when HTTP adaptive streaming players compete for bandwidth,” in Proc. NOSSDAV, pp. 9-14, 2012.
[19] T. Stockhammer. “Dynamic adaptive streaming over http – Standards and design principles,” in Proc. of the Second Annual ACM Conf. on Multimedia Systems (ACM MMSys’2011), pp. 133-144, Feb. 2011.
[20] T. C. Thang, Q.-D. Ho, J. W. Kang, and A. T. Pham. “Adaptive Streaming of Audiovisual Content using MPEG DASH,” IEEE Trans. Consum. Electron., vol. 58, no. 1, pp. 78-85, Feb. 2012.
[21] K. Miller, E. Quacchio, G. Gennari, and A. Wolisz. “Adaptation algorithm for adaptive streaming over http,” in Proc. Packet Video Workshop (PV 2012), pp. 173-178, May 2012.
[22] H. T. Le, D. V. Nguyen, N. P. Ngoc, A. T. Pham, and T. C. Thang.“Buffer-based bitrate adaptation for adaptive http streaming,” in Proc. IEEE ATC2013, Oct. 2013.
[23] J.-C. Jiang, V. Sekar, and H. Zhang. “Improving fairness, efficiency, and stability in HTTP-based adaptive video streaming with FESTIVE,” in Proc. 8th Int. Conf. Emerging Networking Experiments and Tech. (CoNEXT), 2012.
[24] Z. Li, X. Zhu, J. Gahm, R. Pan, H. Hu, A. C. Begen, and D. Oran. “Probe and Adapt: Rate Adaptation for HTTP Video Streaming At Scale,” IEEE J. on Selected Areas in Commun. (J-SAC), Special Issue on Adaptive Media Streaming, 2014.
[25] Zhou, Chao, Xinggong Zhang, and Zongming Guo. “A control theory based rate adaption scheme for dash over multiple servers,” IEEE Visual Communications and Image Processing (VCIP), 2013.
[26] The dummynet project (http://info.iet.unipi.it/~luigi/dummynet).

[27] L. Rizzo. “Dummynet: a simple approach to the evaluation of network protocols,” Comput. Commun. Rev., vol. 27, no. 1, pp. 31-41, 1997.
[28] FFmpeg (https://www.ffmpeg.org).
[29] MP4Box (https://gpac.wp.mines-telecom.fr/mp4box).
[30] S. Lederer, C. Müller, and C. Timmerer. “Dynamic adaptive streaming over http dataset,” in Proc. of ACM MMSYS’12, pp. 89-94, 2012.
[31] Big Buck Bunny (https://peach.blender.org).
[32] Shengkai Zhang, Bo Li, and Baochun Li. “Presto: Towards fair and efficient HTTP adaptive streaming from multiple servers,” IEEE Int. Conf. Communications (ICC), 2015.
[33] C. Lai, H. Chao, Y. Lai and J. Wan, “Cloud-assisted real-time transrating for HTTP live streaming,” IEEE Wireless Commun., vol. 20, no. 3, pp. 62-70, Jun. 2013.
[34] S. Lederer et al. ”Distributed DASH dataset,“ Proceedings of MMSys pp. 131-135 Feb. 2013.
[35] Li Liu, et al. “Probabilistic chunk scheduling approach in parallel multiple-server DASH,” IEEE Visual Communications and Image Processing Conference, 2014 .
[36] Min Xing, Siyuan Xiang, and Lin Cai. “A real-time adaptive algorithm for video streaming over multiple wireless access networks,“ IEEE Journal on Selected Areas in communications 32.4 (2014): 795-805.
[37] Sin-Man Choi, Ximin Huang, and Wai-Ki Ching. “Inducing optimal service capacities via performance-based allocation of demand in a queueing system with multiple servers,“ IEEE Int. Conf. Computers & Ind