Quality of Experience Assessment of Video Quality in Social Clouds

Laghari, Asif Ali; He, Hui; Karim, Shahid; Shah, Himat Ali; Karn, Nabin Kumar

doi:10.1155/2017/8313942

Cited by 47 publications

(27 citation statements)

References 13 publications

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“…In the past, only QoE of different video resolution was assessed considering network parameters form long distance and nearby locations servers [22] and QoE of MP4 file was assessed regarding social cloud compression parameters [23], but QoE was never considered for the comparative study of file formats with different resolution. During the QoE measurement of experimental data, 83 users participated from the department of computer science and information technology and few were from other departments of nontechnical fields such as social sciences, mostly users belonged to undergraduate studies and rest of them were postgraduates.…”

Section: Literature Reviewmentioning

confidence: 99%

Impact of Video File Format on Quality of Experience (QoE) of Multimedia Content

Laghari

Khan

et al. 2018

3D Res

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Video file formats are used to record, play and store videos according to heterogeneous platforms; devices and online streaming on networks vary by speed. Video formats are different codecs and resolution from each other and also vary in display quality and storage size which also differentiate the quality of experience (QoE) of users. This paper presents the subjective QoE of end user's for different video file format varied according to resolution quality and bitrate. Videos of most popular formats which are used for online streaming was downloaded from YouTube and stored on the local system for play for subjects to perceive the video quality. Subjective QoE experiments were conducted by questionnaires given to subjects to perceive video quality and assign ratings. Results show high ratings for FLV 240P and WebM 360P for low resolution videos where high ratings for the MP4 video for 720P and 2160P high resolutions.

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Section: Literature Reviewmentioning

confidence: 99%

Impact of Video File Format on Quality of Experience (QoE) of Multimedia Content

Laghari

Khan

et al. 2018

3D Res

Self Cite

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“…The existing works have addressed different kinds of optimization criteria such as makespan, cost, budget, deadline, resource utilization, throughput, load balancing, and energy efficiency. Generally, these optimization criteria are categorized into two desires based on cloud service: cloud users desire and cloud service providers desire, figure 3 [47]. These optimization criteria are addressed from most of the reviewed works, thus this work tries to demonstrate the way these criteria are studied in a comparative method.…”

Section: Optimization Criteriamentioning

confidence: 99%

Task scheduling in cloud computing based on metaheuristic techniques: A review paper

Al-Arasi¹,

Saif²

2020

EAI Endorsed Transactions on Cloud Systems

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Cloud computing delivers computing resources like software and hardware as a service to the users through a network. Due to the scale of the modern datacentres and their dynamic resources provisioning nature, we need efficient scheduling techniques to manage these resources. The main objective of scheduling is to assign tasks to adequate resources in order to achieve one or more optimization criteria. Scheduling is a challenging issue in the cloud environment, therefore many researchers have attempted to explore an optimal solution for task scheduling in the cloud environment. They have shown that traditional scheduling is not efficient in solving this problem and produce an optimal solution with polynomial time in the cloud environment. However, they introduced sub-optimal solutions within a short period of time. Meta-heuristic techniques have provided near-optimal or optimal solutions within an acceptable time for such problems. In this work, we have introduced the major concepts of resource scheduling and provided a comparative analysis of many task scheduling techniques based on different optimization criteria.

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“…In this section Fog and cloud computing comparison is given in table 1. Comparison table shows that few parameters are same for both Fog and cloud such as portable access, virtualization, multitask, transparency, service negotiation, critical object, number of users support and resources provided by [33,34,55]. Rest of other parameters Fog computing provide more advance benefits compare to cloud computing such as Fog computing provide a response in a short time but cloud takes high.…”

Section: Comparisonmentioning

confidence: 99%

Comparison of Fog Computing & Cloud Computing

Kumar¹,

Laghari²,

Karim³

et al. 2019

IJMSC

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Fog computing is extending cloud computing by transferring computation on the edge of networks such as mobile collaborative devices or fixed nodes with built-in data storage, computing, and communication devices. Fog gives focal points of enhanced proficiency, better security, organize data transfer capacity sparing and versatility. With a specific end goal to give imperative subtle elements of Fog registering, we propose attributes of this region and separate from cloud computing research. Cloud computing is developing innovation which gives figuring assets to a specific assignment on pay per utilize. Cloud computing gives benefit three unique models and the cloud gives shoddy; midway oversaw assets for dependable registering for performing required errands. This paper gives correlation and attributes both Fog and cloud computing differs by outline, arrangement, administrations and devices for associations and clients. This comparison shows that Fog provides more flexible infrastructure and better service of data processing by consuming low network bandwidth instead of shifting whole data to the cloud.

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Quality of Experience Assessment of Video Quality in Social Clouds

Cited by 47 publications

References 13 publications

Impact of Video File Format on Quality of Experience (QoE) of Multimedia Content

Impact of Video File Format on Quality of Experience (QoE) of Multimedia Content

Task scheduling in cloud computing based on metaheuristic techniques: A review paper

Comparison of Fog Computing & Cloud Computing

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