Quantifying the Influence of Rebuffering Interruptions on the User's Quality of Experience During Mobile Video Watching

Pessemier, Toon De; Moor, Katrien De; Joseph, Wout; Marez, Lieven De; Martens, Luc

doi:10.1109/tbc.2012.2220231

Cited by 110 publications

(51 citation statements)

References 23 publications

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“…This problem is the part of a more general topic dealing with waiting times before service consumption and interruptions during the consumption. Although, the subject has been studied for several decades in the domain of market research, in the context of Internet video services it is rather recent issue [13,19]. The re-buffering which takes place in the middle of video playback is usually a consequence of the congestion avoidance algorithm of TCP [5].…”

Section: Quality Measuresmentioning

confidence: 99%

Performance of HTTP video streaming under different network conditions

Biernacki

Tutschku

2013

Multimed Tools Appl

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The Internet video sharing services have been gaining importance and increasing their share in the multimedia market. In order to compete effectively and provide broadcast television with a comparable level of quality, the Internet video should fulfil stringent quality of service (QoS) constraints. However, as the Internet video is based on packet transmission, it is influenced by delays, transmission errors, data losses and bandwidth limitations which can have a devastating influence on the perceived quality of the multimedia content. There are many works which describe the impact of network impairments on the Internet video. Nevertheless, little is known about how network conditions influence the video streamed by the currently popular services such as YouTube, where video is transmitted over reliable TCP/HTTP protocols. Therefore using a network simulator, we conducted an experimental evaluation of the HTTP based video transmission analysing how the network impairments mentioned above influence the streamed video. The experiments were validated against a network emulator supplied with real network traces. As a result of this work, we can state that the buffering strategies implemented by a video player are in many cases able to mitigate unfavourable network conditions what allow to play the streamed video smoothly. The results may serve Internet Service Providers so that they could tune their network characteristics in order to match the demand from HTTP video.

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Section: Quality Measuresmentioning

confidence: 99%

Performance of HTTP video streaming under different network conditions

Biernacki

Tutschku

2013

Multimed Tools Appl

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“…Degraded network conditions may cause empty video buffers, thereby causing video choppiness. Interruptions in the video playback due to rebuffering lead to user frustration and should be avoided if alternative video configurations exists [10].…”

Section: Youtube's Static Optimization Of the Qualitymentioning

confidence: 99%

“…Through this subjective question, users could specify how they perceived the video playback on a 5-point rating scale. Although loading time and interruptions can be measured objectively, the user's opinion is important since not all playback interruptions are perceived as equally disturbing [15], [10]. Table I lists the mean value (MOS) and the standard deviation of the obtained ratings.…”

Section: Users' Quality Feedbackmentioning

confidence: 99%

Dynamic optimization of the quality of experience during mobile video watching

Pessemier

Martens

Joseph

2015

2015 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting

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Abstract-Mobile video consumption through streaming is becoming increasingly popular. The video parameters for an optimal quality are often automatically determined based on device and network conditions. Current mobile video services typically decide on these parameters before starting the video streaming and stick to these parameters during video playback. However in a mobile environment, conditions may change significantly during video playback. Therefore, this paper proposes a dynamic optimization of the quality taking into account realtime data regarding network, device, and user movement during video playback. The optimization method is able to change the video quality level during playback if changing conditions require this. Through a user test, the dynamic optimization is compared with a traditional, static, quality optimization method. The results showed that our optimization can improve the perceived playback and video quality, especially under varying network conditions.

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“…Unfortunately, the QoS parameters do not always reflect the perceived quality of a service perfectly, since they only take into account network related aspects and neglect device characteristics, context parameters, and user aspects. In contrast, subjective quality measurements with actual test subjects can be performed to assess how a service is really perceived by the user, or how the Quality of Experience (QoE) is [10,11].…”

Section: Introductionmentioning

confidence: 99%

Quality assessment and usage behavior of a mobile voice-over-IP service

et al. 2015

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Voice-over-IP (VoIP) services offer users a cheap alternative to the traditional mobile operators to make voice calls. Due to the increased capabilities and connectivity of mobile devices, these VoIP services are becoming increasingly popular on the mobile platform. Understanding the user's usage behavior and quality assessment of the VoIP service plays a key role in optimizing the Quality of Experience (QoE) and making the service to succeed or to fail.By analyzing the usage and quality assessments of a commercial VoIP service, this paper identifies device characteristics, context parameters, and user aspects that influence the usage behavior and experience during VoIP calls. Whereas multimedia services are traditionally evaluated by monitoring usage and quality for a limited number of test subjects and during a limited evaluation period, this study analyzes the service usage and quality assessments of more than thousand users over a period of 120 days. This allows to analyze evolutions in the usage behavior and perceived quality over time, which has not been done up to now for a widelyused, mobile, multimedia service. The results show a significant evolution over time of the number of calls, the call duration, and the quality assessment. The time of the call, the used network, and handovers during the call showed to have a significant influence on the users' quality assessments.Keywords usage behavior · user experience · voiceover-ip · mobile · quality assessment 1 Introduction

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Quantifying the Influence of Rebuffering Interruptions on the User's Quality of Experience During Mobile Video Watching

Cited by 110 publications

References 23 publications

Performance of HTTP video streaming under different network conditions

Performance of HTTP video streaming under different network conditions

Dynamic optimization of the quality of experience during mobile video watching

Quality assessment and usage behavior of a mobile voice-over-IP service

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