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研究生:簡友琳
研究生(外文):Yu-Lin Chien
論文名稱:基於機器學習方法之HTTP 串流速率調節機制
論文名稱(外文):Machine Learning Based Rate Adaptation with Elastic Feature Selection for HTTP-Based Streaming
指導教授:陳銘憲陳銘憲引用關係
指導教授(外文):Ming-Syan Chen
口試委員:林靖茹蔡欣穆楊得年
口試委員(外文):Ching-Ju LinHsin-Mu TsaiDe-Nian Yang
口試日期:2014-12-23
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:電機工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:英文
論文頁數:28
中文關鍵詞:HTTP 串流速率調節機器學習
外文關鍵詞:HTTP StreamingRate AdaptationMachine Learning
相關次數:
  • 被引用被引用:0
  • 點閱點閱:154
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  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
Dynamic Adaptive Streaming over HTTP (DASH) 於現在已經成為一
個越來越重要的應用。影響HTTP 上串流影音品質最重要的關鍵,就
在於如何選擇適當的影片速率調節機制。之前的一些相關論文提出
一些可以根據目前網路狀態的變化,來動態調整下載影片速率的方
法;但是會影響到影片速率選擇的因素有許多種,而這些方法一般都
只考慮其中少數的幾個重要因素,像是預測的頻寬或是目前緩衝影
片的長度。但是頻寬預測不僅相當困難,同時容易有很大誤差可能,
而這導致了其可能嚴重影響到速率選擇的效果。為了解決這個問題,
我們提出了於HTTP 上基於機器學習的速率調節機制(MLASH)。利
用classification 的方法,MLASH 不僅可以有彈性的將所有可能影響
到速率調節的因素都考慮進來,還可以避開頻寬預測的困難。同時,
MLASH 還可以與之前的其他速率調節方法進行整合,並且利用大數
據的特性,來進一步提升速率調節之效果。我們根據原始資料來進行
模擬實驗,以證明我們的方法不僅效果良好,同時於不同的使用者體
驗衡量標準上,表現也比之前其他的速率調節方法更加優秀。

Dynamic Adaptive Streaming over HTTP (DASH) has become an emerging
application nowadays. Video rate adaptation is a key to determine the
video quality of HTTP-based media streaming. Recent works have proposed
several algorithms that allow a DASH client to adapt its video encoding rate to
network dynamics. While network conditions are typically affected by many
different factors, these algorithms however usually consider only a few representative
information, e.g., predicted available bandwidth or fullness of its
playback buffer. In addition, the error in bandwidth estimation could significantly
degrade their performance. Therefore, this paper presents Machine-
Learning-based Adaptive Streaming over HTTP (MLASH), an elastic framework
that exploits a wide range of useful network-related features to train
a rate classification model. The distinct properties of MLASH are that its
machine-learning-based framework can be incorporated with any existing adaptation
algorithm and utilize big data characteristics to improve prediction accuracy.
We show via trace-based simulations that machine-learning-based
adaptation can achieve a better performance than traditional adaptation algorithms
in terms of their target quality of experience (QoE) metrics.

中文摘要i
Abstract ii
Contents iii
List of Figures v
1 Introduction 1
2 Related Work and Background 3
2.1 QoE Metrics and Rate adaptation algorithm . . . . . . . . . . . . . . . . 3
2.2 TCP throughput / bandwidth prediction . . . . . . . . . . . . . . . . . . 4
2.3 Resource allocation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
3 MLASH Design 6
3.1 System Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
3.2 Feature Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.3 Model Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
4 Trace-based Evaluation 12
4.1 Performance Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.2 Variable bitrate scenario . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.3 Convergence of Model Training . . . . . . . . . . . . . . . . . . . . . . 21
5 Conclusion 23
Bibliography 24

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