隨著現今科技快速成長，網際網路的多媒體應用也蓬勃發展，因此IPTV（Internet Protocol Television）成為受矚目的焦點之一，IPTV有別於一般傳統的有線電視，是將存放在雲端伺服器內的頻道內容，透過IP寬頻網路提供使用者觀看電視的服務，為了使IPTV更加普及，使用者的滿意度是很重要的議題，許多研究指出IPTV滿意度指標最主要的是頻道切換時間，也就是當使用者從目前觀看的頻道，轉換到另一台之間的等待時間。
先前的研究大多針對傳統的IPTV架構做改進，傳統的IPTV架構是建立在雲端架構上，雲端伺服器內存放著頻道內容，而目前最新研究提出雲端架構的延伸概念，稱為霧端架構，因此本論文提出一個IPTV-F架構，將IPTV與霧端架構結合，用來改進IPTV的效能以及使用者的滿意度。除了提出IPTV-F架構，我們還提出一個IPTV-Fb方法，選出最佳的霧端伺服器，來傳送頻道內容給使用者觀看。
本論文使用NS2來建立模擬環境，從三種不同變因來測量不同選擇方法下的頻道切換時間，模擬結果顯示出IPTV-Fb優於另外兩種選擇方法，一種是雲端架構下的方法（稱為IPTV-C），另一種是霧端架構，但用隨機選擇霧端伺服器的方法（稱為IPTV-Fr）。從模擬結果可看出IPTV-Fb優於另外兩種選擇方式，改善率會隨著使用者數量增加而降低；隨著霧端伺服器數量增加而增加；隨著頻寬變異數增加而有小幅度增加。

With the rapid growth of technology, IPTV (Internet Protocol Television) become more popular. Differing from the traditional cable TV, IPTV stores the contents in the cloud server, and provides channel contents via IP broadband Internet to users. In order to make IPTV more popular, users’ satisfaction is a very important issue. A lot of researches indicate that the most important IPTV QoS is the channel zapping time, that is, when a user changes the current channel until he/she can view the content of the next channel.
Most previous researches want to improve IPTV QoS on the traditional IPTV architecture, which is built on the cloud infrastructure. Currently the researches proposed a new concept of extending the cloud architecture, called fog architecture. Therefore, this paper presents IPTV-F architecture to combine IPTV with the fog architecture, in order to improve the effectiveness of IPTV and users’ satisfaction. In addition to proposing the IPTV-F architecture, we propose an algorithm, namely IPTV-Fb, to select the best fog server to deliver channel contents to the user.
This paper uses NS2 to build a simulation environment, and then compares the channel zapping among different approaches. The simulation results show that IPTV-Fb selection method is superior to the IPTV-C and IPTV-Fr, where IPTV-C is on the cloud architecture and IPTV-Fr is on the fog architecture but randomly selects the fog server. According to the simulation result, IPTV-Fb is the best approaches than the others. The improvement ratio decreases as the number of users increases, increases as the number of fog servers increases, and increases as the variance of bandwidth increases.

[1]http://globalmedia.wikia.com/wiki/%E5%8F%B0%E7%81%A3%E7%B6%B2%E8%B7%AF%E9%9B%BB%E8%A6%96iptv%E7%94%A2%E6%A5%AD
[2]C. Y. Lee, C. K. Hong, and K. Y. Lee, "Reducing Channel Zapping Time in IPTV Based on User’s Channel Selection Behaviors," IEEE Transactions on Broadcasting, vol. 56, pp. 321-330, 2010.
[3]Z. Jiang, D.S. Chan, M.S. Prabhu, P. Natarajan, H. Hao, and F. Bonomi, "Improving Web Sites Performance Using Edge Servers in Fog Computing Architecture," 2013 IEEE 7th International Symposium on Service Oriented System Engineering （SOSE）, pp. 320-323, 2013.
[4]R. Kooij, K. Ahmed, and K. Brunnström, "Perceived Quality of Channel Zapping," Proceedings of the Fifth IASTED International Conferences - COMMUNICATION SYSTEMS AND NETWORKS, pp. 155-158, Aug. 2006.
[5]ITU-T Rec. P.800, "Methods for Subjective Determination of Transmission Quality," International Telecommunication Union, Telecommunication standardization sector, 1996.
[6]J. Lee, G. Lee, S. Seok, and B. Unknown, "Advanced Scheme to Reduce IPTV Channel Zapping Time," Managing Next Generation Networks and Services, vol. 4773, pp. 235-243, 2007.
[7]S. Xiaowei, "An Intelligent Recommendation System Based on Fuzzy Logic," Information in Control, Automation and Robotics, pp. 105-109, 2006.
[8]L. Ardissono, C. Gena, P. Torasso, F. Bellifemine, A. Difino, and B. Negro, "User Modeling and Recommendation Techniques for Personalized lectronic Program Guides," Personalized Digital Television – Targeting Programs to Individual Viewers, vol. 6, pp. 3-26, 2004.
[9]Y. Lee, J. Lee, I. Kim, and H. Unknown, "Reducing IPTV Channel Switching Time using H.264 Scalable Video Coding," IEEE Transactions on Consumer Electronics, vol. 54, pp. 912-919, May 2008.
[10]S. K. Mandal and M. Mburu, "Intelligent Pre-fetching to Reduce Channel Switching Delay in IPTV Systems," Texas A & M, 2008, http://students.cs.tamu.edu/skmandal/research/channelswitching.pdf.
[11]F. Bonomi, R. Milito, J Zhu, and S. Addepalli, "Fog Computing and Its Role in the Internet of Things," MCC '12 Proceedings of the first edition of the MCC workshop on Mobile cloud computing, pp. 13-16, Aug. 2012.
[12]I. Stojmenovic and S. Wen, "The Fog Paradigm: Scenarios and Security Issues," Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, vol. 2, pp. 1-8, 2014.