Paper Info
Title
Performance Analysis of Adaptive Bitrate Algorithms for Multi-user DASH Video Streaming
Abstract
With the increasing video demand in daily network traffic, it is an urgent task to develop effective algorithms to facilitate high-quality content delivery service. Recently, numerous adaptive streaming algorithms have been proposed to improve the user perceived experience. However, these algorithms were mainly developed from the perspective of single user. There is not yet systematical evaluation and comparison of the bitrate adaptation methods for multi-user video streaming. Besides, the Quality of Experience (QoE) metrics were not unified. In this work, we propose a new mininet-based testbed framework which is able to conduct real-time video streaming emulation in various multi-user scenarios. Seven state-of-the-art adaptation methods are incorporated into the testbed. Meanwhile, ITU-T P.1203 model, the world's first standard for measuring QoE of HTTP adaptive streaming, is implemented to calculate the mean opinion scores of different methods. Using the developed testbed, the performance of current adaptation methods in multi-user network is analyzed and compared. A variety of experiments are carried out by changing the user number and network conditions, in which the QoE of different users are investigated. It is found that current algorithms perform inconsistently in various network scenarios. In the excessive user and limited bandwidth cases, machine learning and scheduling techniques show superiority in providing high and equal QoE for all users. While in the high-delay case, the buffer-based approaches show robust performance. Overall, the findings of this work give an insight for designing and choosing adaptive streaming strategies in different multi-user network conditions.
Year
DOI
Venue
2021
10.1109/WCNC49053.2021.9417599
2021 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC)
Keywords
DocType
ISSN
HTTP adaptive streaming, bitrate adaptation algorithm, multi-user, mean opinion score, QoE
Conference
1525-3511
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
bo wei15814.91
Song Hang211.02
Shangguang Wang381688.84
Jiro Katto426266.14