Quantifying Video-QoE Degradations of Internet Links

Venkataraman, M.; Chatterjee, Mainak

doi:10.1109/tnet.2011.2167684

Cited by 17 publications

(10 citation statements)

References 21 publications

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“…In [9] intra-and inter-ISP links were measured to identify issues affecting video streaming QoE. The findings show that most of the issues originate from fluctuations in intradomain links, however there is no clear correlation of these problems with QoE.…”

Section: Related Workmentioning

confidence: 99%

Identifying the root cause of video streaming issues on mobile devices

Dimopoulos

Leontiadis

Barlet‐Ros

et al. 2015

Proceedings of the 11th ACM Conference on Emerging Networking Experiments and Technologies

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Video streaming on mobile devices is prone to a multitude of faults and although well established video Quality of Experience (QoE) metrics such as stall frequency are a good indicator of the problems perceived by the user, they do not provide any insights about the nature of the problem nor where it has occurred. Quantifying the correlation between the aforementioned faults and the users' experience is a challenging task due the large number of variables and the numerous points-of-failure.To address this problem, we developed a framework for diagnosing the root cause of mobile video QoE issues with the aid of machine learning. Our solution can take advantage of information collected at multiple vantage points between the video server and the mobile device to pinpoint the source of the problem. Moreover, our design works for different video types (e.g., bitrate, duration, ..) and contexts (e.g., wireless technology, encryption, ..) After training the system with a series of simulated faults in the lab, we analyzed the performance of each vantage point separately and when combined, in controlled and real world deployments. In both cases we find that the involved entities can independently detect QoE issues and that only a few vantage points are required to identify a problem's location and nature.

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Section: Related Workmentioning

confidence: 99%

Identifying the root cause of video streaming issues on mobile devices

Dimopoulos

Leontiadis

Barlet‐Ros

et al. 2015

Proceedings of the 11th ACM Conference on Emerging Networking Experiments and Technologies

View full text Add to dashboard Cite

show abstract

“…There have also been large scale studies looking at MPEG-2 video streaming [24] and Skype [31] [19] traffic. These studies examine the link between network QoS, specifically latency, jitter and packet loss, and QoE as measured by mean opinion scores from user surveys.…”

Section: Related Workmentioning

confidence: 99%

Scalable classification of QoS for real-time interactive applications from IP traffic measurements

Middleton

Modafferi

2016

Computer Networks

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Measurement of network Quality of Service (QoS) has attracted considerable research effort over the last two decades. The recent trend towards Internet Service Providers (ISP's) offering application-specific QoS is creating possibilities for more sophisticated QoS metrics to be offered by ISP's in service level agreements. This in turn could be used for the purposes of improved network optimization and billing according to application specific QoS guarantees. We report a scalable near real-time approach using passively logging IP traffic data for classification of application latency and packet loss across a range of real-time interactive applications. We run six experiments involving Minecraft, Quake 3 Urban Terror, VLC video streaming and the commercial Wirofon VOIP application. We use a mixture of laboratory and realworld deployments, with run times ranging from hours to days, and observe a combination of real and simulated ISP latency and packet loss events. Our binary classification (i.e. classes 'OK' or 'lag') 10-fold cross validation F1 scores are between 0.80 and 0.93 depending on application type. Our multi-class classification (i.e. classes representing discrete packet loss or latency ranges) 10-fold cross validation F1 scores for Minecraft are 0.89 for latency and 0.90 for packet loss. With new business models between ISP's and application developers being actively considered this work represents a significant contribution to the debate by providing scientific evidence relating to a novel approach to scalable QoS measurement

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“…If jitter is present the time between consecutive packets is unknown prior to delivery. In Internet traffic jitter can be of the order of hundreds of milliseconds which has the potential to distort the video [3]. Figure 1 shows the effects jitter can have on continuity.…”

Section: Effect Of Jitter To Perceptual Video Qualitymentioning

confidence: 99%

“…Although network capacity has increased rapidly, a commonly encountered problem is bandwidth fluctuation. Indeed, an increase/decrease of bandwidth availability causes jitter or the variation of inter-arrival time between consecutive packets [1][2][3][4]. Jitter results in an increase in the number of frames arriving at their destination after play-out time, i.e.…”

Section: Introductionmentioning

confidence: 99%

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Continuity-Aware Scheduling Algorithm for Scalable Video Streaming

2016

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Abstract:The consumer demand for retrieving and delivering visual content through consumer electronic devices has increased rapidly in recent years. The quality of video in packet networks is susceptible to certain traffic characteristics: average bandwidth availability, loss, delay and delay variation (jitter). This paper presents a scheduling algorithm that modifies the stream of scalable video to combat jitter. The algorithm provides unequal look-ahead by safeguarding the base layer (without the need for overhead) of the scalable video. The results of the experiments show that our scheduling algorithm reduces the number of frames with a violated deadline and significantly improves the continuity of the video stream without compromising the average Y Peek Signal-to-Noise Ratio (PSNR).

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Quantifying Video-QoE Degradations of Internet Links

Cited by 17 publications

References 21 publications

Identifying the root cause of video streaming issues on mobile devices

Identifying the root cause of video streaming issues on mobile devices

Scalable classification of QoS for real-time interactive applications from IP traffic measurements

Continuity-Aware Scheduling Algorithm for Scalable Video Streaming

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