scholarly journals VCC-DASH: A Video Content Complexity-Aware DASH Bitrate Adaptation Strategy

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 230
Author(s):  
Juzheng Duan ◽  
Min Zhang ◽  
Jing Wang ◽  
Shuai Han ◽  
Xun Chen ◽  
...  
Keyword(s):  
Quality Of Experience ◽  
Adaptation Strategy ◽  
Video Content ◽  
Buffer Occupancy ◽  
Network Bandwidth ◽  
Opinion Score ◽  
Hypertext Transfer Protocol ◽  
Simulation Results ◽  
Definition Of

Traditional DASH (dynamic adaptation streaming over HTTP(i.e., HyperText Transfer Protocol)) bitrate strategy cannot differentiate segments with different complexities of video content, resulting in the user’s QoE (quality of experience) of segments with high content complexity as worse than that with low content complexity. In case of this, this paper firstly studies video coding and puts forward the definition of video content complexity. Then the effects of content complexity on user’s QoE is analyzed and the QoE utility function of the segment is formulated based on its MOS (mean opinion score, related to the content complexity and bitrate) and bitrate switching between consecutive segments. Last, in order to maximize user’s QoE, this paper proposes VCC-DASH (video content complexity-aware DASH bitrate adaptation strategy) under the constraints of the network bandwidth and the buffer occupancy. In simulations, we compare VCC-DASH with the classical bitrate adaptation strategy proposed by Liu et al. (LIU’s strategy, for short). The simulation results show that the two strategies have similar performances in bitrate switching numbers, playback interruption times, and buffer lengths. In addition, it is more important for simulation results to reveal that VCC-DASH’s average bitrate is much higher than that of LIU’s strategy, which means that VCC-DASH can make fuller use of the network bandwidth than LIU’s strategy does. Moreover, the MOS distribution of the VCC-DASH is more concentrated on the better scores “4~5”, which profit from its content complexity-aware adaptation to allocate more bandwidth resources to high-complexity segments.

scholarly journals A Window-Based, Server-Assisted P2P Network for VoD Services with QoE Guarantees

2017 ◽  
Vol 2017 ◽  
pp. 1-18 ◽  
Author(s):  
Noé Torres-Cruz ◽  
Mario E. Rivero-Angeles ◽  
Gerardo Rubino ◽  
Ricardo Menchaca-Mendez ◽  
Rolando Menchaca-Mendez
Keyword(s):  
Mobile Networks ◽  
Quality Of Experience ◽  
Fluid Model ◽  
Video On Demand ◽  
P2p Network ◽  
Video Content ◽  
5G Networks ◽  
Video File ◽  
Definition Of

We describe a Peer-to-Peer (P2P) network that is designed to support Video on Demand (VoD) services. This network is based on a video-file sharing mechanism that classifies peers according to the window (segment of the file) that they are downloading. This classification easily allows identifying peers that are able to share windows among them, so one of our major contributions is the definition of a mechanism that could be implemented to efficiently distribute video content in future 5G networks. Considering that cooperation among peers can be insufficient to guarantee an appropriate system performance, we also propose that this network must be assisted by upload bandwidth from servers; since these resources represent an extra cost to the service provider, especially in mobile networks, we complement our work by defining a scheme that efficiently allocates them only to those peers that are in windows with resources scarcity (we called it prioritized windows distribution scheme). On the basis of a fluid model and a Markov chain, we also developed a methodology that allows us to select the system parameters values (e.g., windows sizes or minimum servers upload bandwidth) that satisfy a set of Quality of Experience (QoE) parameters.

scholarly journals User Quality of Experience (QoE) Satisfaction for Video Content Selection (VCS) Framework in Smartphone Devices

2021 ◽  
Vol 18 (4(Suppl.)) ◽  
pp. 1387
Author(s):  
Muhamad Hanif Jofri ◽  
Ida Aryanie Bahrudin ◽  
Noor Zuraidin Mohd Safar ◽  
Juliana Mohamed ◽  
Abdul Halim Omar
Keyword(s):  
Video Streaming ◽  
User Satisfaction ◽  
Quality Of Experience ◽  
Video Quality ◽  
Video Content ◽  
Selection Scheme ◽  
Content Selection ◽  
Opinion Score ◽  
Quality Video

Video streaming is widely available nowadays. Moreover, since the pandemic hit all across the globe, many people stayed home and used streaming services for news, education,  and entertainment. However,   when streaming in session, user Quality of Experience (QoE) is unsatisfied with the video content selection while streaming on smartphone devices. Users are often irritated by unpredictable video quality format displays on their smartphone devices. In this paper, we proposed a framework video selection scheme that targets to increase QoE user satisfaction. We used a video content selection algorithm to map the video selection that satisfies the user the most regarding streaming quality. Video Content Selection (VCS) are classified into video attributes groups. The level of VCS streaming will gradually decrease to consider the least video selection that users will not accept depending on video quality. To evaluate the satisfaction level, we used the Mean Opinion Score (MOS) to measure the adaptability of user acceptance towards video streaming quality. The final results show that the proposed algorithm shows that the user satisfies the video selection, by altering the video attributes.

scholarly journals Nonintrusive Method Based on Neural Networks for Video Quality of Experience Assessment

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Diego José Luis Botia Valderrama ◽  
Natalia Gaviria Gómez
Keyword(s):  
Neural Networks ◽  
Quality Of Experience ◽  
Video Quality ◽  
Similarity Index ◽  
Correlation Coefficients ◽  
Structural Similarity ◽  
Opinion Score ◽  
Measurement And Evaluation ◽  
Rate Correlation

The measurement and evaluation of the QoE (Quality of Experience) have become one of the main focuses in the telecommunications to provide services with the expected quality for their users. However, factors like the network parameters and codification can affect the quality of video, limiting the correlation between the objective and subjective metrics. The above increases the complexity to evaluate the real quality of video perceived by users. In this paper, a model based on artificial neural networks such as BPNNs (Backpropagation Neural Networks) and the RNNs (Random Neural Networks) is applied to evaluate the subjective quality metrics MOS (Mean Opinion Score) and the PSNR (Peak Signal Noise Ratio), SSIM (Structural Similarity Index Metric), VQM (Video Quality Metric), and QIBF (Quality Index Based Frame). The proposed model allows establishing the QoS (Quality of Service) based in the strategyDiffserv. The metrics were analyzed through Pearson’s and Spearman’s correlation coefficients, RMSE (Root Mean Square Error), and outliers rate. Correlation values greater than 90% were obtained for all the evaluated metrics.

scholarly journals Virtual User in the IoT: Definition, Technologies and Experiments

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4489
Author(s):  
Roberto Girau ◽  
Raimondo Cossu ◽  
Massimo Farina ◽  
Virginia Pilloni ◽  
Luigi Atzori
Keyword(s):  
Quality Of Experience ◽  
User Profile ◽  
Physical World ◽  
Legal Issues ◽  
User Profiling ◽  
Context Management ◽  
Management Quality ◽  
Definition Of ◽  
The Internet Of Things

Virtualization technologies are characterizing major advancements in the Internet of Things (IoT) arena, as they allow for achieving a cyber-physical world where everything can be found, activated, probed, interconnected, and updated at both the virtual and the physical levels. We believe these technologies should apply to human users other than things, bringing us the concept of the Virtual User (VU). This should represent the virtual counterpart of the IoT users with the ultimate goal of: (i) avoiding the user from having the burden of following the tedious processes of setting, configuring and updating IoT services the user is involved in; (ii) acting on behalf of the user when basic operations are required; (iii) exploiting to the best of its ability the IoT potentialities, always taking always account the user profile and interests. Accordingly, the VU is a complex representation of the user and acts as a proxy in between the virtual objects and IoT services and application; to this, it includes the following major functionalities: user profiling, authorization management, quality of experience modeling and management, social networking and context management. In this respect, the major contributions of this paper are to: provide the definition of VU, present the major functionalities, discuss the legal issues related to its introduction, provide some implementation details, and analyze key performance aspects in terms of the capability of the VU to correctly identify the user profile and context.

Impact of video content and transmission impairments on quality of experience

2016 ◽  
Vol 75 (23) ◽  
pp. 16461-16485 ◽  
Author(s):  
Pradip Paudyal ◽  
Federica Battisti ◽  
Marco Carli
Keyword(s):  
Quality Of Experience ◽  
Video Content ◽  
Transmission Impairments

scholarly journals A CQI based Novel Shared Channel Downlink Scheduler for LTE Networks

2019 ◽  
Vol 8 (6) ◽  
pp. 4422-4426
Keyword(s):  
High Speed ◽  
Quality Of Experience ◽  
Multimedia Content ◽  
Video Content ◽  
Lte Networks ◽  
Downlink Scheduling ◽  
High Data ◽  
Shared Channel ◽  
New Generation

Long Terrn Evolution (LTE) can be called as the new generation of High Speed Cellular Communication. LTE networks serves as back bone for 4G networks delivering high data transmission speeds and support for Qualisty of Service (QoS). It also ensures the availability of high speed connection, HD Calling, more security and extended support for streaming of HD multimedia content which includes audio and video content. With this much development in the field of mobile communication, another term was coined QoE (Quality of experience) which refers to the overall degree of acceptability of the multimedia content as perceived by the end users. In this paper we introduce a CQI based algorithm to improve the overall QoE while it is being applied on downlink scheduling. Simulation runs proves that CQI has better results as compared to other algorithms based upon parameters such as throughput, SnR and fairness.

scholarly journals A quality of experience-aware cross-layer architecture for optimizing video streaming services

2020 ◽  
Author(s):  
qahhar muhammad qadir ◽  
Alexander A. Kist ◽  
ZHONGWEI ZHANG
Keyword(s):  
Video Streaming ◽  
Quality Of Experience ◽  
Video Quality ◽  
The Internet ◽  
Cross Layer ◽  
Video Traffic ◽  
Adaptive Architecture ◽  
Streaming Services ◽  
Simulation Results

The popularity of the video services on the Internet has evolved various mechanisms that target the Quality of Experience (QoE) optimization of video traffic. The video quality has been enhanced through adapting the sending bitrates. However, rate adaptation alone is not sufficient for maintaining a good video QoE when congestion occurs. This paper presents a cross-layer architecture for video streaming that is QoE-aware. It combines adaptation capabilities of video applications and QoE-aware admission control to optimize the trade-off relationship between QoE and the number of admitted sessions. Simulation results showed the efficiency of the proposed architecture in terms of QoE and number of sessions compared to two other architectures (adaptive architecture and non-adaptive architecture ).

scholarly journals Analysis of the Cost of Varying Levels of User Perceived Quality for Internet Access

2020 ◽  
Author(s):  
Ali Adib Arnab ◽  
John Schormans ◽  
Sheikh Razibulhasan Raj ◽  
Nafi Ahmad
Keyword(s):  
Quality Of Service ◽  
User Experience ◽  
Quality Of Experience ◽  
Internet Access ◽  
End User ◽  
Opinion Score ◽  
Commercial Organizations ◽  
The Cost ◽  
Qos Metrics

Quality of Service (QoS) metrics deal with network quantities, e.g. latency and loss, whereas Quality of Experience (QoE) provides a proxy metric for end-user experience. Many papers in the literature have proposed mappings between various QoS metrics and QoE. This paper goes further in providing analysis for QoE versus bandwidth cost. We measure QoE using the widely accepted Mean Opinion Score (MOS) rating. Our results naturally show that increasing bandwidth increases MOS. However, we extend this understanding by providing analysis for internet access scenarios, using TCP, and varying the number of TCP sources multiplexed together. For these target scenarios our analysis indicates what MOS increase you get by further expenditure on bandwidth. We anticipate that this will be of considerable value to commercial organizations responsible for bandwidth purchase and allocation.

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