Measurement And Improvement of Quality-of-Experience For Online Video Streaming Services
Abstract
HTTP based online video streaming services have been consistently dominating
the online traffic for the past few years. Measuring and improving the performance of
these services is an important challenge. Traditional Quality-of-Service (QoS) metrics
such as packet loss, jitter and delay which were used for networked services are not easily
understood by the users. Instead, Quality-of-Experience (QoE) metrics which capture the
overall satisfaction are more suitable for measuring the quality as perceived by the users.
However, these QoE metrics have not yet been standardized and their measurement and
improvement poses unique challenges. In this work we first present a comprehensive
survey of the different set of QoE metrics and the measurement methodologies suitable
for HTTP based online video streaming services.
We then present our active QoE measurement tool Pytomo that measures the QoE
of YouTube videos. A case study on the measurement of QoE of YouTube videos when
accessed by residential users from three different Internet Service Providers (ISP) in a
metropolitan area is discussed. This is the first work that has collected QoE data from
actual residential users using active measurements for YouTube videos. Based on these
measurements we were able to study and compare the QoE of YouTube videos across
multiple ISPs. We also were able to correlate the QoE observed with the server clusters
used for the different users. Based on this correlation we were able to identify the server
clusters that were experiencing diminished QoE.
DynamicAdaptive Streaming overHTTP (DASH) is an HTTP based video streaming
that enables the video players to adapt the video quality based on the network conditions.
We next present a rate adaptation algorithm that improves the QoE of DASH
video streaming services that selects the most optimum video quality. With DASH the
video server hosts multiple representation of the same video and each representation is
divided into small segments of constant playback duration. The DASH player downloads
the appropriate representation based on the network conditions, thus, adapting the video
quality to match the conditions. Currently deployed Adaptive Bitrate (ABR) algorithms
use throughput and buffer occupancy to predict segment fetch times. These algorithms
assume that the segments are of equal size. However, due to the encoding schemes employed
this assumption does not hold. In order to overcome these limitations, we propose
a novel Segment Aware Rate Adaptation algorithm (SARA) that leverages the knowledge
of the segment size variations to improve the prediction of segment fetch times. Using
an emulated player in a geographically distributed virtual network setup, we compare the
performance of SARA with existing ABR algorithms. We demonstrate that SARA helps
to improve the QoE of the DASH video streaming with improved convergence time, better
bitrate switching performance and better video quality. We also show that unlike the existing
adaptation schemes, SARA provides a consistent QoE irrespective of the segment
size distributions.
Table of Contents
Introduction -- Measurement of QoE for Online Video Streaming Services: A Literature Survey -- Pytomo: A Tool for measuring QoE of YouTube Videos -- Case Study: QoE across three Internet Service Providers in a Metropolitan Area -- Adaptive Bitrate Algorithms for DASH -- Segment Aware Rate Adaptation for DASH -- Performance Evaluation of SARA -- Conclusion and Future Research --Appendix A. Sample MPD File
Degree
Ph.D.