隨著網路的演進，網路影音串流逐漸的被普遍使用，像是YouTube和LiveTV，並且根據Cisco的分析預測，在2018年，網路影音串流將占據整個網路79%的使用量，可得知未來網路資源將會頻繁的使用在影音串流，但是影音串流需求的網路頻寬會隨著品質(解析度)的不同會有很大的變化，像是1080P影片的需求頻寬可能是720P影片的兩倍之多，這代表著在一個有限的網路資源環境下，如何有效的利用頻寬來達到最佳的使用者接受度將是一個逐漸重要的議題，此外，關於無線網路的發展，例如WiFi和4G，也是一個有趣的議題，無線網路會隨著使用者的增加，網路頻寬將不斷的被競爭，此時的頻寬會產生很大的變動，這對於網路影音串流來說是一個很大的威脅，因為不同解析度的影音大小是差異很大的，如果選擇過大的影音片段並發生網路的資源競爭，這可能會造成影音的播放中斷，另外就是在cellular network(例如4G)的環境下，不同的基地台所可以提供的頻寬也是有很大的差異，如果在handover時，連到了一台只能提供較低頻寬的基地台，這也會造成影音串流中斷的危險，由上述議題，我們設計了一個系統來提供無線網路的影音服務，考慮到無線網路的趨勢和使用者目前的影音預載量(buffer)來決定下一個影音串流片段的解析度，避免影片的意外中斷並提供使用者最佳的體驗，此外，我們還考量到每片段影音實際的頻寬需求，例如影片在動態的武打畫面和靜態的談話畫面會有很大不同的頻寬需求，如果可以更精確的去了解整個影片各片段的頻寬需求分佈，這將可以更有效的利用頻寬和維持影音品質。

With the tremendous growth in the volume of video traffic in mobile networks with restricted network bandwidth and fluctuating channel quality, it becomes a critical issue to effectively adapt video streaming in wireless networks to support better video streaming performance. Dynamic Adaptive Streaming over HTTP (DASH) is the most common solution to adapt video bit rate according to available bandwidth to improve video performance. However, DASH selects video versions only based on the average bit rate of a whole video, which is a coarse-grained streaming scheme, instead of exploiting the variable bit rates of a video. Thus, DASH cannot adapt video streaming properly enough to maintain video quality in unstable channel environments. In this thesis, we propose a Profile-based Adaptive Video Streaming (PAVS) scheme to effectively adapt video streaming in wireless networks. Compared to DASH, PAVS retrieves the average bit rate of each video segment to get fine-grained video information. Moreover, PAVS estimates the expected bandwidth in wireless networks by tracking TCP ACKs from clients and using the historical network bandwidth information between a video server and clients; then, it selects the proper video version according to the fine-grained video information, the expected bandwidth, and buffer occupancy of the client. In summary, the PAVS provides a fine-grained video version selection and minimizes control message in resource restricted wireless networks. Compared to DASH, based on our experimented results, PAVS reduces the number of video re-buffering events by 76% and the total time of video re-buffering by 81%. Besides, PAVS increases the bandwidth utilization of adapting video streaming to 84%.

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