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 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 Statistical Error Propagation Affecting the Quality of Experience Evaluation in Video on Demand Applications

2020 ◽  
Vol 10 (10) ◽  
pp. 3662 ◽  
Author(s):  
Abdul Wahab ◽  
Nafi Ahmad ◽  
John Schormans
Keyword(s):  
Error Propagation ◽  
Quality Of Experience ◽  
Statistical Error ◽  
Video On Demand ◽  
Sampling Errors ◽  
On Demand ◽  
Subjective Testing ◽  
Qos Parameters ◽  
Latency Jitter

In addition to the traditional Quality of Service (QoS) metrics of latency, jitter and Packet Loss Ratio (PLR), Quality of Experience (QoE) is now widely accepted as a numerical proxy for the actual user experience. The literature has reported many mathematical mappings between QoE and QoS, where the QoS parameters are measured by the network providers using sampling. Previous research has focussed on sampling errors in QoS measurements. However, the propagation of these sampling errors in QoS through to the QoE values has not been evaluated before. This is important: without knowing how sampling errors propagate through to QoE estimates there is no understanding of the precision of the estimates of QoE, only of the average QoE value. In this paper, we used industrially acquired measurements of PLR and jitter to evaluate the sampling errors. Additionally, we evaluated the correlation between these QoS measurements, as this correlation affects errors propagating to the estimated QoE. Focusing on Video-on-Demand (VoD) applications, we use subjective testing and regression to map QoE metrics onto PLR and jitter. The resulting mathematical functions, and the theory of error propagation, were used to evaluate the error propagated to QoE. This error in estimated QoE was represented as confidence interval width. Using the guidelines of UK government for sampling in a busy hour, our results indicate that confidence intervals around estimated the Mean Opinion Score (MOS) rating of QoE can be between MOS = 1 to MOS = 4 at targeted operating points of the QoS parameters. These results are a new perspective on QoE evaluation and are of potentially great significance to all organisations that need to estimate the QoE of VoD applications precisely.

A Novel Flash P2P Network Traffic Prediction Algorithm based on ELMD and Garch

2020 ◽  
Vol 19 (01) ◽  
pp. 127-141
Author(s):  
Yimu Ji ◽  
Ye Wu ◽  
Dianchao Zhang ◽  
Yongge Yuan ◽  
Shangdong Liu ◽  
...  
Keyword(s):  
Network Traffic ◽  
Network Performance ◽  
Video On Demand ◽  
P2p Network ◽  
Prediction Algorithm ◽  
Campus Network ◽  
Local Mean Decomposition ◽  
Traffic Rate ◽  
Local Mean

To improve the quality of service and network performance for the Flash P2P video-on-demand, the prediction Flash P2P network traffic flow is beneficial for the control of the network video traffic. In this paper, a novel prediction algorithm to forecast the traffic rate of Flash P2P video is proposed. This algorithm is based on the combination of the ensemble local mean decomposition (ELMD) and the generalized autoregressive conditional heteroscedasticity (GARCH). The ELMD is used to decompose the original long-related flow into the summation of the short-related flow. Then, the GRACH is utilized to predict the short-related flow. The developed algorithm is tested in a university’s campus network. The predicted results show that our proposed method can further achieve higher accuracy than those obtained by existing algorithms, such as GARCH and Local Mean Decomposition and Generalized AutoRegressive Conditional Heteroskedasticity (LMD-GARCH) while keeping lower computational complexity.

Measurement of Quality of Experience of Video-on-Demand Services: A Survey

2016 ◽  
Vol 18 (1) ◽  
pp. 401-418 ◽  
Author(s):  
Parikshit Juluri ◽  
Venkatesh Tamarapalli ◽  
Deep Medhi
Keyword(s):  
Quality Of Experience ◽  
Video On Demand ◽  
On Demand

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
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