User perception of adapting video quality

Cranley, Nicola; Perry, Philip; Murphy, Liam

doi:10.1016/j.ijhcs.2005.12.002

Cited by 121 publications

(78 citation statements)

References 12 publications

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“…As standardized method, only the Single Stimulus Continuous Quality Evaluation (SSCQE) method, specified in [1] and for instance used in [8,27], is addressing long test sequences by enabling instantaneous evaluation of quality for the whole sequence. Recency and hysteresis effects of the human behavioral responses while continuously evaluating the time-varying video quality were observed [28].…”

Section: New Problems: Open Questions and Lack Of Methodsmentioning

confidence: 99%

Quality of experience and HTTP adaptive streaming: A review of subjective studies

Garcia

Simone

Tavakoli

et al. 2014

2014 Sixth International Workshop on Quality of Multimedia Experience (QoMEX)

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HTTP adaptive streaming technology has become widely spread in multimedia services because of its ability to provide adaptation to characteristics of various viewing devices and dynamic network conditions. There are various studies targeting the optimization of adaptation strategy. However, in order to provide an optimal viewing experience to the end-user, it is crucial to get knowledge about the Quality of Experience (QoE) of different adaptation schemes. This paper overviews the state of the art concerning subjective evaluation of adaptive streaming QoE and highlights the challenges and open research questions related to QoE assessment.

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Section: New Problems: Open Questions and Lack Of Methodsmentioning

confidence: 99%

Quality of experience and HTTP adaptive streaming: A review of subjective studies

Garcia

Simone

Tavakoli

et al. 2014

2014 Sixth International Workshop on Quality of Multimedia Experience (QoMEX)

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“…By observing how much time the Markov process spends in each state, and how often the process transitions between states, the mean video bitrate and the number of quality switches are obtained. These two factors play a major role in the viewers' QoE [3,5,15].…”

Section: Evaluating Bandwidth Sharing Policiesmentioning

confidence: 99%

Enhancing Over-the-Top Video Streaming Quality with DASH Assisting Network Elements

Kleinrouweler

2017

Adjunct Publication of the 2017 ACM International Conference on Interactive Experiences for TV and Online Video

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Dynamic Adaptive Streaming over HTTP (DASH) is the leading technology for delivering online video streaming content. However, DASH has performance problems on shared network links. This thesis investigates how DASH Assisting Network Elements (DANEs) can be used to optimize bottleneck links for DASH video traffic, with the goal to improve the viewers' Quality of Experience. DANEs are aware of active DASH players and divide the network bandwidth among the players and other traffic. In the first three years of the PhD, contributions have been made in the areas of multimedia systems and performance modeling: Two prototype implementations of DANEs have been developed and evaluated in both wired and Wi-Fi networks. Experiments with real DASH players show that DANEs significantly increase the video bitrate and reduce the number of changes in video quality. In addition, Markov models have been created to find out how network bandwidth should be divided, and what the effect of bandwidth sharing policies is on the resulting streaming performance. The model was thoroughly evaluated and has shown to be highly accurate. As such, it is a useful tool that can be used to configure and optimize bandwidth sharing in DANEs. In the remaining year of the PhD program, I would like to expand DANE technology and apply it to different use cases such as mobile networks.

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“…Furthermore, it is also worth noting that a constant and continuous increment of the video rate may not always enhance QoE. In [3] it was demonstrated that when the video quality is high an increase in the current rate does not necessarily translate into an improvement in the user-perceived quality. Nevertheless, the paper made an important observation: when the buffer is used as the main factor of an ABR the trade-off between video quality and the amount of rebuffering is unnecessary.…”

Section: Background and Related Workmentioning

confidence: 99%

“…an abrupt drop of the video quality), which is known to be detrimental to QoE [17,25] and to minimise the negative impact of recency effect [5,9], transition decision to q k+1 should depend on q k . Furthermore, since users are not known to be appreciative of an increase in the video quality when the video rate is relatively high [3] we recommend a nonlinear g ′ (R). In fact, Yamagishi and Hayashi [24] have shown that, given a set of linearly incremented video rates, the saturation begins to take effect at about halfway through the available video rates, when they studied the relationship between video rate and the subjective video quality.…”

Section: Background and Related Workmentioning

confidence: 99%

“…However, a linear increment of video rate, as used in [7], may not always enhance the QoE. For example, when the video quality is relatively high an increase in the current video rate does not necessarily translate into an improvement in the user-perceived quality, since as shown in [3,17] a stage is always reached when users do not find any further increase beneficial. Furthermore, the buffer management model employed in [7,8] artificially separates the ramping-up period from the steady state, which results in significant loss of quality.…”

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confidence: 99%

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On the trajectory of video quality transition in HTTP adaptive video streaming

2017

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browsers, TVs. Each of these platforms has specific requirements with respect to transmission and video quality. Moreover, the environment within which most of the video streaming clients operate is both unreliable and varies over time. However, regardless of the access device, users want the best viewing experience possible. HTTP adaptive streaming (HAS) is the most successful technology so far that allows content providers to cater for the requirements of the multitude of devices and contexts. The process through which a HAS client chooses a video rate is called adaptive bitrate selection (ABR). The first generation of ABRs relied on throughput estimation and selected the highest video rate lower than the measured throughput [14]. This is based on the work of Wang et al. [22] that showed if the available TCP throughput is twice the bitrate of the video plus a few seconds of start-up delay TCP can ensure an acceptable video streaming experience. It later became clear that throughput estimation alone is not a sufficient parameter for designing efficient ABR since an accurate bandwidth estimation above the HTTP layer is difficult to achieve [6]. Consequently, any video rate selection algorithm that solely depends on such a relatively inaccurate estimate results in unnecessary rebuffering events [7], an undesirable variability of video rate [6] and sub-optimal video quality [6].Various attempts have been made to improve some of the identified issues of throughput-based ABRs by supplementing throughput measurements with information about the playback buffer [1,10,21]. Using buffer occupancy as a factor in video rate selection has developed from regarding buffer state changes as a complementary factor in making a rate selection decision [10,21] to employing it as the sole metric [7,8]. Though, whatever factor an ABR primarily relies on, it is difficult to build an ABR that maximises Quality of Experience (QoE) without taking buffer state changes into consideration.Abstract HTTP adaptive video streaming matches video quality to the capacity of a changing context. A variety of schemes that rely on buffer state dynamics for video rate selection have been proposed. However, these schemes are predominantly based on heuristics, and appropriate models describing the relationship between video rate and buffer levels have not received sufficient attention. In this paper, we present a QoE-aware video rate evolution model based on buffer state changes. The scheme is evaluated within a real-world Internet environment. The results of an extensive evaluation show an improvement in the stability, average video rate and system utilisation, while at the same time a reduction in the start-up delay and convergence time is achieved by the modified players.

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User perception of adapting video quality

Cited by 121 publications

References 12 publications

Quality of experience and HTTP adaptive streaming: A review of subjective studies

Quality of experience and HTTP adaptive streaming: A review of subjective studies

Enhancing Over-the-Top Video Streaming Quality with DASH Assisting Network Elements

On the trajectory of video quality transition in HTTP adaptive video streaming

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