基於機器學習方法之HTTP 串流速率調節機制

Abstract

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.