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中国科技论文在线
QoE Evaluation for Adaptive Streaming Based on
Psychological Recency Effect
LIU Qianhong, LIU Yitong, YANG Dacheng
**
(Wireless Theory and Technology lab, Beijing University of Posts and Telecommunications, 5
Beijing, 100876)
Brief author introduction:Qianhong Liu(1990-), Male, Main research: QoE research on multimedia service
Correspondance author: Yang Dacheng(1951-),Male,Professor,Main research: Wireless Communicaitons Theory
and Technology. E-mail: yangdc@bupt.edu.cn
Abstract: A new method based on the recency effect, a psychological phenomenon that the recent
information is more prominent in short-term memory, is put forward for the QoE evaluation of adaptive
streaming, which takes bitrate adaption and users’ experiencing habits into account. Different from the
traditional solutions, the new method uses the divide-and-conquer strategy to simplify the QoE
10
evaluation of adaptive streaming into calculating scores of CBR (Constant Bit Rate) segments and
integrating these scores to give the final QoE evaluation based on the recency effect. A subjective test
has been conducted to confirm the great influence of the recency effect on the integral QoE evaluation,
and then a mathematical model is developed to measure its influence. Based on the model, a solution is
proposed to integrate the scores of CBR segments into the QoE of adaptive streaming service. As a
15
result, the Pearson correlation coefficient between the scores of the new method and the subjective
MOS reaches 0.953, and the new method is verified to be sensitive to bitrate adaption.
Key words: Comunication and Information system, the Recency effect, DASH, QoE, subjective test
0 Introduction
With the development of communication technology and the popularization of mobile 20
platforms, there is an explosive demand for high-quality video service. In a report by Cisco
[1]
,the
real-time video takes 50% of the Internet traffic at peak periods, and it is predicted that by 2015,
various forms of video will exceed 90 percent of global consumer traffic, and almost 66 percent of
the world’s mobile traffic will be video. As a result, video communication over mobile networks
brings great challenges due to limitations in bandwidth and difficulties in maintaining high 25
reliability, quality, and latency demands. At present, one of the key solutions is adaptive streaming
which is an increasingly promising method to deliver video to end users allowing enhancements in
QoE (Quality of Experience) and network bandwidth efficiency.
There exists various adaptive streaming solutions and the industry is undergoing
standardizing solutions referred to as DASH
[2]
(Dynamic Adaptive Streaming over HTTP). DASH 30
presets new challenges and opportunities for content developers, service providers, network
operators and device manufactures. One of these important challenges is to develop evaluation
methodologies and performance metrics to accurately assess user QoE for adaptive streaming
services, and effectively utilizing these metrics for service provisioning and optimizing network
adaptation. 35
Currently, the research about this aspect is rather limited. The mainstream is to extract
metrics (KPIs, KQIs, e.g.) from network and image parameters, and integrate them into a score.
For example, in
[3]
, packet size, transmission time and bit rate are considered to measure the
representation level. Another effective method is the application of neural network
[4]
. However,
these existing methods seem to ignore the influence of bitrate distribution caused by rate adaption, 40
the most direct and the most obvious characteristic of adaptive streaming.
Perhaps, a new approach can be achieved from users’ viewing behavior. When viewing an
adaptive streaming service, viewers’ feelings fluctuate with its time-varying quality in real-time