臺灣博碩士論文加值系統

近年來，各式網路應用服務蓬勃發展，網際網路協定電視(IPTV)儼然成為新世代網路的殺手級應用，主因為IPTV不只提供服務，更著眼於提供具有足夠品質的服務。然而，IPTV於實現傳統電視的特性有一定的挑戰性，換台時間過長為其重要的議題之一，因此本論文要透過預收頻道降低換台時間，以滿足使用者的使用經驗。
雖說過去已有許多相關研究藉由預收頻道來加快換台，但他們都對使用者的轉台行為進行假設，如此當使用者不是以其預設轉台行為進行轉台，那麼預收頻道將平白浪費；另外，他們都只利用機上盒(STB)自行選擇頻道並預收，但網路頻寬是有限的，若網路頻寬小於所有要求頻道的頻寬需求，則很可能造成觀看品質低落，因此IPTV業者一般會使用允入控制器限制所有STB最大可存取之頻道數，這使得STB的要求頻道可能被拒絕，輕則預收頻道被拒絕而降低預收頻道命中率，重則觀看頻道被拒絕，降低使用經驗，且提高阻塞率。
基於以上兩點，本研究提出一個近期行為優先估測法及其變體，藉由收集使用者的近期轉台資訊，估測使用者的轉台行為及其頻道偏好，並統計出使用者下次會前往每個頻道的機率，然而頻寬有限，直接選擇使用者最可能前往的兩個頻道來預收的話，可能被拒絕，因此還使用一個換台伺服器(Channel Change Server，CCS)收集所有STB的統計資訊，以保證STB總是能成功預收2個頻道。
從模擬結果發現，由近期行為優先估測法之變體估測出的結果，其準確度及精確度都隨著收集的樣本增加而變高；在不同的使用者轉台行為假設下，本研究提出的機制也擁有相同或較高的命中率；即使在頻寬有限的環境，也能提供較高的命中率，並藉由CCS避免因預收頻道而提升阻塞率。

In recent years, more and more network service are booming, especially IPTV. IPTV has already become a new killer application since it does not only provide the service, but it also provides the service with the required quality. However, to realize the characteristics of traditional TV is a challenge to IPTV, and long channel change time is one of the important issues. For this reason, this paper makes efforts in reducing channel change time by prejoining some channels, to meet the user experience.
Some related works had presented their mechanisms to prejoin channels, but theirs are based on the assumptions of user’s browsing behavior. In this case, the bandwidth consumed by prejoined channels is wasted if a user changes channel by not their default behaviors. Besides, they make STB choose channels and prejoin itself. However, the network bandwidth is limited, and it is likely to lowering the video quality if the bandwidth is insufficient. Consequently, IPTV operators would use an Admission Control (AC) to restrict the maximum joined channels of all STBs which results in the poor hit rate of prejoined channels or the watched channels being blocked at worst.
Based on the above, this paper presents an estimator called Recent-First-Estimator and its variation to estimate a user’s browsing behavior and his channel preference by collecting user’s browsing data, and figure out the probabilities of changing to each channel in his next channel change. Even so, the requested channels for prejoin may be rejected due to limited bandwidth if STB choose the most probable 2 channels to prejoin. We use a Channel Change Server (CCS) as AC to gather the statistics of all STBs and ensure that STB can always prejoin 2 channels.
From the simulation results, the accuracy and the precision of the estimated value becomes high with the increase of the collected samples. Under different assumptions of the browsing behavior, our prejoin strategy has the same or higher hit rate and prevents prejoined channels from increasing blocking rate by the help of CCS even in the bandwidth-limited environment.

中文摘要..................................................I
ABSTRACT................................................III
誌謝......................................................V
目錄.....................................................VI
表目錄.................................................VIII
圖目錄...................................................IX
CHAPTER1 序論.............................................1
1.1 簡介..................................................1
1.2 動機..................................................2
1.3 論文組織架構..........................................5
CHAPTER2 研究背景.........................................6
2.1 IPTV使用者經驗品質(QOE)...............................6
2.2 相關研究..............................................6
2.2.1. 預收鄰近頻道.......................................7
2.2.2. 收集使用者資訊以預收頻道...........................8
2.2.3. 使用環狀緩衝區與高速串流加速換台..................12
2.3 問題描述.............................................12
CHAPTER3 研究方法........................................16
3.1 目的.................................................16
3.2 網路架構.............................................16
3.3 使用者轉台行為及其頻道偏好...........................18
3.4 假設條件.............................................19
3.4.1. STB預收頻道並事先解碼.............................20
3.4.2. 網路頻寬有限......................................20
3.4.3. 使用者具有顯著偏好的轉台行為......................21
3.5 行為預測機制.........................................21
3.5.1. 收集使用者資訊....................................21
3.5.2. 問題定義..........................................21
3.5.3. 直接取平均的估測法................................22
3.5.4. 近期行為優先估測法................................23
3.5.5. 近期行為優先估測法的變體..........................24
3.6 頻道預收策略.........................................26
3.6.1. 單一使用者的預收策略..............................27
3.6.2. 頻寬有限情況下的多使用者預收策略..................27
CHAPTER4 分析與模擬......................................32
4.1 分析.................................................32
4.1.1. 單一使用者的預收頻道命中率........................32
4.1.2. 換台時間與轉台結果的關係..........................34
4.2 模擬設定.............................................36
4.2.1. 參數定義與設定....................................36
4.2.2. 變數定義..........................................41
4.2.3. 不同機制的環境設定................................42
4.3 模擬結果.............................................43
4.3.1. Bayes-RFE的估測效能...............................43
4.3.2. 單一使用者的預收頻道命中率比較....................45
4.3.3. 多使用者的預收頻道命中率與阻塞率比較..............51
4.3.4. 給定使用者人數的頻寬需求模擬評估..................56
CHAPTER5 結論與後續研究..................................59
5.1 結論.................................................59
5.2 後續研究.............................................59
參考文獻.................................................60

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