Assessing QoE of on-demand TCP video streams in real time

Dalal, Amy Csizmar; Bouchard, Andy K.; Cantor, Sara; Guo, Yiran; Johnson, Anya Elaine

doi:10.1109/icc.2012.6364073

Cited by 10 publications

(8 citation statements)

References 11 publications

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“…Previous work [7], [11] demonstrated that certain pieces of stream state information are more valuable than others in terms of influencing video QoE. The presence of additional stream state data can introduce "noise" into the ratings.…”

Section: System Architecturementioning

confidence: 99%

“…Our approach, which uses data mining to process application-layer, stream state measurements (bandwidth, frame rate, and the number of received packets), and compare these to previouslyobtained subjective ratings, is able to assess video quality with between 75 and 87% accuracy on just 15 seconds of application-layer measurements [7].…”

Section: Introductionmentioning

confidence: 99%

“…In previous work, we have demonstrated that we can evaluate the QoE of on-demand video in real time using objective measurements collected from two instrumented media players, Windows Media Player [6], [7] and Flash Player [8]. Our approach, which uses data mining to process application-layer, stream state measurements (bandwidth, frame rate, and the number of received packets), and compare these to previouslyobtained subjective ratings, is able to assess video quality with between 75 and 87% accuracy on just 15 seconds of application-layer measurements [7].…”

Section: Introductionmentioning

confidence: 99%

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Systems considerations in real time video QoE assessment

Dalal

2012

2012 IEEE Globecom Workshops

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We consider several key questions in the design of a real time video quality assessment system. How frequently can we generate subjective video quality ratings with some degree of accuracy? How often should we sample the data? How do we weigh the need to consolidate data collection (arguing for fewer, less frequent data points) with the need to monitor video quality in real time (arguing for more frequent data points)? What are the timing requirements for such a system, both in training the system and in assigning ratings to videos? Our results demonstrate that we can achieve accurate video quality ratings by using small portions of the video and a frequent sampling rate, and that in the worst case the system can be trained in ten minutes. 1

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Section: System Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Systems considerations in real time video QoE assessment

Dalal

2012

2012 IEEE Globecom Workshops

Self Cite

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“…The player was developed for RTP streaming over UDP, which was topical at the time. In [3], Dalal et al extended their research to include also RTP over TCP. The ultimate goal of their work was to spot degraded video quality experience (QoE) in real time and, before the end user notices any change in video quality, affect the conditions giving rise to the degraded quality.…”

Section: Related Workmentioning

confidence: 99%

On the Students’ Qos-Awareness of Video Lectures

Hakala¹,

Härmänmaa²,

Laine³

2016

EDULEARN Proceedings

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Video lectures bring flexibility and enable distance learning in education. With the help of videos, educational organizations are able to serve a wider and more heterogeneous group of students. As streaming videos have become essential part of teaching in higher education, quality of service (QoS) issues should be taken more into consideration. However, systematic quality monitoring of video lecture delivery, to our knowledge, is rarely used in a way that would provide also the student with quality information. This paper describes our ongoing research on developing a streaming video quality evaluation tool integrated in a learning environment that our master's students use. The tool serves both the education provider and the students.

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“…User rating buttons (1,2,3,4,5) The user is instructed to press one of the horizontally aligned five (based on ACR scale) user rating (UR) buttons (see Fig. 5c) whenever the user feels like rating the quality.…”

Section: User Interfacementioning

confidence: 99%

VLQoE: Video QoE instrumentation on the smartphone

Ickin

Fiedler

Wac

et al. 2014

Multimed Tools Appl

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The usage of network-demanding applications is growing rapidly such as video streaming on mobile terminals. However, network and/or service providers might not guarantee the perceived quality for video streaming that demands high packet transmission rate. In order to satisfy the user expectations and to minimize user churn, it is important for network operators to infer the end-user perceived quality in video streaming. Today, the most reliable method to obtain end-user perceived quality is through subjective tests, and the preferred location is the user interface as it is the closest point of application to the enduser. The end-user perceived quality on video streaming is highly influenced by occasional freezes; technically the extraordinary time gaps between two consecutive pictures that are displayed to the user, i.e., high inter-picture time. In this paper, we present a QoE instrumentation for video streaming, VLQoE. We added functionality to the VLC player to record Multimed Tools Appl a set of metrics from the user interface, application-level, network-level, and from the available sensors of the device. To the best of our knowledge, VLQoE is the first tool of its kind that can be used in user experiments for video streaming. By using the tool, we present a two state model based on the inter-picture time, for the HTTP-and RTSP-based video streaming via 3.5G. Next, we studied the influence of inter-picture time on the user perceived quality through out a user study. We investigated the minimum user perceived inter-picture time, and the user response time.

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Assessing QoE of on-demand TCP video streams in real time

Cited by 10 publications

References 11 publications

Systems considerations in real time video QoE assessment

Systems considerations in real time video QoE assessment

On the Students’ Qos-Awareness of Video Lectures

VLQoE: Video QoE instrumentation on the smartphone

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