scholarly journals Memories of Video: Impact of Sequencing on Rated Technical Quality for Viewed and Visualized Disruptions

2019 ◽  
Vol 63 (1) ◽  
pp. 386-390
Author(s):  
Thomas He ◽  
Chelsea DeGuzman ◽  
Leon Zucherman ◽  
Tiffany Tong ◽  
Mark Chignell
Keyword(s):  
Quality Of Experience ◽  
Service Providers ◽  
Streaming Video ◽  
Technical Quality ◽  
End Effect ◽  
Content Quality ◽  
Relative Change ◽  
The Impact

In this paper we explore how memories of experience with streaming video affect Quality of Experience (QoE) indicators that are of interest to service providers and marketers. Since observations of experience are time consuming, and the effects of technical quality (TQ) are difficult to entangle from content quality (CQ), we examined the impact of a visualization methodology for assessing experiences. A study was carried out to examine how well overall technical quality (TQ) judgments for a sequence of visualized video experience (a picture of a red video playbar with yellow portions indicating disrupted video in place of actually viewed video) would correspond to overall TQ judgments made after watching a sequence of actual videos. Sequencing effects found in overall TQ ratings, made after viewing visualizations (with their overlaid disruptions) were similar to sequencing effects found after viewing actual videos. However, the sequencing effects after viewing the visualizations were less pronounced than the corresponding sequencing effects that were found after viewing actual videos. Sequences of both visualized and actually viewed videos showed significant negative end effect and trend effects (both positive and negative). There was also evidence that sequencing effects respond to relative change in TQ rather than absolute TQ.

Testing the Effects of Peak, End, and Linear Trend on Evaluations of Online Video Quality of Experience

2017 ◽  
Vol 61 (1) ◽  
pp. 813-817 ◽  
Author(s):  
Chelsea DeGuzman ◽  
Mark Chignell ◽  
Jie Jiang ◽  
Leon Zucherman
Keyword(s):  
Quality Of Experience ◽  
Service Providers ◽  
Linear Trend ◽  
Video Quality ◽  
Peak Effect ◽  
Online Video ◽  
End Effect ◽  
Feature Difference ◽  
The Impact

While previous research has shown that the sequencing of good and bad experience is an important predictor of overall evaluations of a set of experiences, the impact of sequencing effects on the experience of viewing online video has yet to be established. The aim of this study was to determine whether different sequences of good (G), mediocre (M), and bad (B) quality videos in different blocks would influence overall ratings after viewing those blocks. Thirty-two participants each watched 10 blocks of 4 videos and provided ratings of technical quality (TQ), satisfaction, and frustration for each video in the block, as well as overall ratings for each block (as a whole). Sequences of G, M, and B videos were designed to test whether block characteristics (features), like the peak-end effect and effect of linear trend, influenced summary evaluations of the block service. The results of the experiment show that overall block TQ, satisfaction, and frustration ratings differed significantly by sequencing feature. Difference scores were used to determine whether the features had an effect on overall evaluations beyond what could be explained by the total number of bad videos in the block or the average ratings of the videos in the block. Results showed a significant end effect for negative ends of a block, and an effect of linear trend (both increasing and decreasing). There was no evidence of a peak effect or an end effect for positive ends. The presence of a negative end effect and effect of linear trend indicate that where possible service providers should avoid service sessions with poor service quality at the end, or sessions that have decreasing quality as the session progresses.

scholarly journals A Longitudinal Study on Quality of Experience (QoE) measures to predict customer’s Likelihood to Recommend (L2R) a service

2019 ◽  
Vol 63 (1) ◽  
pp. 148-152
Author(s):  
Amin Azad ◽  
Mark Chignell ◽  
Leon Zucherman
Keyword(s):  
Longitudinal Study ◽  
Quality Of Experience ◽  
Technical Quality ◽  
Single Session ◽  
Short Period ◽  
Youtube Videos ◽  
The Impact ◽  
Over Time

Models that predict satisfaction with a service over time need to consider the impact of emotions and remembered quality of experience in predicting overall attitudes towards a service. However, prior research on subjective quality of experience has typically focused on experiments conducted in a single session or over a short period of time. Thus, there is a gap between our understanding of instantaneous quality of experience and long-term judgments, such as overall satisfaction, and likelihood to recommend and likelihood to churn. The goal of the study reported here was to carry out a longitudinal study that would provide initial insights into how experiences of service quality over time are accumulated into memories that then drive longer term attitudes about the service. Our longitudinal study was carried out over a period of roughly 4 weeks with around 3 sessions per week. To facilitate the study, an online service was constructed that would let participants search through YouTube videos, and that added impairments (specified according to an overall experimental design) to the videos before they were played. Participants were asked to rate several measures, including Technical Quality, after each video was viewed. They were also asked to give overall impressions after each session of five videos had been viewed. The results were analyzed in terms of both sequencing effects within sessions. and memory effects that carried over between sessions.

scholarly journals An Adaptive Bitrate Switching Algorithm for Speech Applications in Context of WebRTC

2021 ◽  
Vol 17 (4) ◽  
pp. 1-21
Author(s):  
Mohannad Alahmadi ◽  
Peter Pocta ◽  
Hugh Melvin
Keyword(s):  
Real Time ◽  
Data Exchange ◽  
Quality Of Experience ◽  
End User ◽  
High Quality ◽  
Content Type ◽  
Switching Algorithm ◽  
Wide Range ◽  
The Impact

Web Real-Time Communication (WebRTC) combines a set of standards and technologies to enable high-quality audio, video, and auxiliary data exchange in web browsers and mobile applications. It enables peer-to-peer multimedia sessions over IP networks without the need for additional plugins. The Opus codec, which is deployed as the default audio codec for speech and music streaming in WebRTC, supports a wide range of bitrates. This range of bitrates covers narrowband, wideband, and super-wideband up to fullband bandwidths. Users of IP-based telephony always demand high-quality audio. In addition to users’ expectation, their emotional state, content type, and many other psychological factors; network quality of service; and distortions introduced at the end terminals could determine their quality of experience. To measure the quality experienced by the end user for voice transmission service, the E-model standardized in the ITU-T Rec. G.107 (a narrowband version), ITU-T Rec. G.107.1 (a wideband version), and the most recent ITU-T Rec. G.107.2 extension for the super-wideband E-model can be used. In this work, we present a quality of experience model built on the E-model to measure the impact of coding and packet loss to assess the quality perceived by the end user in WebRTC speech applications. Based on the computed Mean Opinion Score, a real-time adaptive codec parameter switching mechanism is used to switch to the most optimum codec bitrate under the present network conditions. We present the evaluation results to show the effectiveness of the proposed approach when compared with the default codec configuration in WebRTC.

Quality of Experience (QoE) for Wireless Video Over Critical Communication Systems

2017 ◽  
pp. 132-167
Author(s):  
Emad Danish ◽  
Mazin I. Alshamrani
Keyword(s):  
Resource Allocation ◽  
Quality Of Service ◽  
Energy Consumption ◽  
Communication Systems ◽  
Quality Of Experience ◽  
Service Providers ◽  
Wireless Video ◽  
User Centric ◽  
Near Future

Video streaming is expected to acquire a massive share of the global internet traffic in the near future. Meanwhile, it is expected that most of the global traffic will be carried over wireless networks. This trend translates into considerable challenges for Service Providers (SP) in terms of maintaining consumers' Quality of Experience (QoE), energy consumption, utilisation of wireless resources, and profitability. However, the majority of Radio Resource Allocation (RRA) algorithms only consider enhancing Quality of Service (QoS) and network parameters. Since this approach may end up with unsatisfied customers in the future, it is essential to develop innovative RRA algorithms that adopt a user-centric approach based on users' QoE. This chapter focus on wireless video over Critical communication systems that are inspired by QoE perceived by end users. This chapter presents a background to introduce the reader to this area, followed by a review of the related up-to-date literature.

Quality of Internet Service as Experienced by Tanzanians in 2016

2017 ◽  
Vol 9 (4) ◽  
pp. 12-31
Author(s):  
Eliamani Sedoyeka
Keyword(s):  
Social Media ◽  
Quality Of Experience ◽  
Service Providers ◽  
Qualitative Approach ◽  
Internet Service ◽  
Policy Makers ◽  
Internet Users ◽  
Telecommunication Companies ◽  
Article Quality

In this article, Quality of Experience (QoE) is discussed as experienced by Tanzanian internet users for the second biannual of 2016. It presents findings of the research that aimed at among other things, finding out the QoE in internet services offered by telecommunication companies and other internet service providers in the country. A qualitative approach was used to establish practical quality of experience issues considered important by Tanzanians. Online questionnaires distributed over social media mainly WhatsApp and Facebook were used to ask users about their experiences of the services they had been receiving, in which over 2000 responses were collected from all districts of Tanzania. It was established that usability, quality of service, price and after sale support were the main issues found to influence quality of experience for many. The findings in this article are useful for academicians, QoS and QoE researchers, policy makers and ICT professionals.

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