scholarly journals Developing a Video Buffer Framework for Video Streaming in Cellular Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Saba Qasim Jabbar ◽  
Dheyaa Jasim Kadhim ◽  
Yu Li
Keyword(s):  
Outage Probability ◽  
Video Streaming ◽  
Cellular Networks ◽  
Quality Of Experience ◽  
Video Quality ◽  
Base Station ◽  
System Throughput ◽  
Selection Algorithm ◽  
Radio Resources

This work proposes a new video buffer framework (VBF) to acquire a favorable quality of experience (QoE) for video streaming in cellular networks. The proposed framework consists of three main parts: client selection algorithm, categorization method, and distribution mechanism. The client selection algorithm was named independent client selection algorithm (ICSA), which is proposed to select the best clients who have less interfering effects on video quality and recognize the clients’ urgency based on buffer occupancy level. In the categorization method, each frame in the video buffer is given a specific number for better estimation of the playout outage probability, so it can efficiently handle so many frames from different videos at different bitrates. Meanwhile, at the proposed distribution mechanism, a predetermined threshold value is selected for lower and upper levels of playout outage probability. Then, the control unit at the base station will distribute the radio resources and decide the minimum rate requirement based on clients’ urgency categories. Simulation results showed that the VBF grantees fairness of resources distribution among different clients within the same cellular network while minimizing the interruption duration and controlling the video buffer at an acceptable level. Also, the results showed that the system throughput of the proposed framework outperforms other existing algorithms such as playout buffer and discontinuous reception aware scheduling (PBDAS), maximum carrier-to-interface ratio (MAX-CIR), and proportional fair (PF) due to enhancing the quality of experience for video streaming by increasing the radio resources in fairness manner.

scholarly journals Personalized Online Live Video Streaming Using Softmax-Based Multinomial Classification

2019 ◽  
Vol 9 (11) ◽  
pp. 2297
Author(s):  
Kyeongseon Kim ◽  
Dohyun Kwon ◽  
Joongheon Kim ◽  
Aziz Mohaisen
Keyword(s):  
Video Streaming ◽  
Waiting Time ◽  
Quality Of Experience ◽  
Video Quality ◽  
Live Video ◽  
Streaming Services ◽  
Online Streaming ◽  
Over The Top ◽  
User Content

As the demand for over-the-top and online streaming services exponentially increases, many techniques for Quality of Experience (QoE) provisioning have been studied. Users can take actions (e.g., skipping) while streaming a video. Therefore, we should consider the viewing pattern of users rather than the network condition or video quality. In this context, we propose a proactive content-loading algorithm for improving per-user personalized preferences using multinomial softmax classification. Based on experimental results, the proposed algorithm has a personalized per-user content waiting time that is significantly lower than that of competing algorithms.

scholarly journals Quality of Experience Measurement for Video Streaming Based On Adaptive Neural Fuzzy Inference System

2019 ◽  
pp. 1609-1617
Author(s):  
Rana Fareed Ghani ◽  
Amal Sufiuh Ajrash
Keyword(s):  
Video Streaming ◽  
Fuzzy Inference System ◽  
Quality Of Experience ◽  
Fuzzy Inference ◽  
Technological Development ◽  
Video Quality ◽  
Quality Measurement ◽  
Inference System ◽  
Neural Fuzzy

Technological development in recent years leads to increase the access speed in the networks that allow a huge number of users watching videos online. Video streaming is one of the most popular applications in networking systems. Quality of Experience (QoE) measurement for transmitted video streaming may deal with data transmission problems such as packet loss and delay. This may affect video quality and leads to time consuming. We have developed an objective video quality measurement algorithm that uses different features, which affect video quality. The proposed algorithm has been estimated the subjective video quality with suitable accuracy. In this work, a video QoE estimation metric for video streaming services is presented where the proposed metric does not require information on the original video. This work predicts QoE of videos by extracting features. Two types of features have been used, pixel-based features and network-based features. These features have been used to train an Adaptive Neural Fuzzy Inference System (ANFIS) to estimate the video QoE. 

scholarly journals Survey on TYDER Based Algorithm for Radio Resource Allocation in 5G Heterogeneous Scenarios

2020 ◽  
Author(s):  
Premi A ◽  
Rajakumar S
Keyword(s):  
Resource Allocation ◽  
Cellular Networks ◽  
Mobile Networks ◽  
Base Station ◽  
Radio Resource ◽  
Machine To Machine ◽  
Generation Network ◽  
Radio Resources ◽  
Massive Number

The rapid growth of machine-to-machine communications in cellular networks poses the challenge of meeting the various Quality-of-Service requirements of massive number of machine to machine communications devices with limited radio resources. In this study, we discuss the minimum resource allocation problem for M2M communications through 5G and beyond the cellular networks. Then, in 5G mobile networks we propose a TYDER based algorithm for allocation the radio resource. The next-generation network environment, associated with heterogeneous performance, is expected to include the networks of diverse types. This paper introduces the network Traffic Type-based Differentiated Reputation (TYDER) solution, which differentiates the data delivery process according to its type.This approach however requires creativity in the reduction of hardware and cost decrease in the plan of little cell base station.

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 Influence of Resolution and Frame Rate on the Linear In-Stream Video Ad QoE

2014 ◽  
Vol 7 (1) ◽  
Author(s):  
Miloš Ljubojević ◽  
Vojkan Vasković ◽  
Zdenka Babić ◽  
Dušan Starčević
Keyword(s):  
Video Streaming ◽  
Quality Of Experience ◽  
Objective Assessment ◽  
Video Quality ◽  
Rate Change ◽  
Frame Rate ◽  
Strong Impact ◽  
Video Advertising ◽  
The Impact

Abstract: An increasing number of services and facilities that are of interest to users is based on video streaming. Technical characteristics of video have a strong impact on the quality of a video streaming service and its perception by users. The most important measure of quality, which focuses on the user, is the Quality of Experience (QoE). Given that video advertising is a typical video streaming application, it is necessary to analyze the effect of the change of video characteristics on the QoE. This paper examines the impact of resolution and frame rate change on the QoE level by using objective and subjective QoE metrics. It also looks at the possibility of mapping the objective QoE metrics into subjective ones, if the QoE in Internet video advertising is analyzed. It was demonstrated that the values obtained by the objective assessment of quality can be mapped to the results obtained by subjective assessment of quality when the quality of experience of linear in- stream video ads is analyzed. The results indicate that temporal aspects of video quality assessment, e.g. influence of resolution and frame rate change to the level of the QoE, can be achieved by implementation of objective methods. Therefore, quality of experience can be improved by the proper selection of video characteristics values.

DUPLICATE: Drone cellular networks: Enhancing the quality of experience of video streaming applications

2018 ◽  
Vol 80 ◽  
pp. 130-141 ◽  
Author(s):  
Ludovico Ferranti ◽  
Francesca Cuomo ◽  
Stefania Colonnese ◽  
Tommaso Melodia
Keyword(s):  
Video Streaming ◽  
Cellular Networks ◽  
Quality Of Experience ◽  
Streaming Applications

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.

Optimizing Quality-of-Experience for HTTP-based Adaptive Video Streaming

2016 ◽  
Vol 7 (4) ◽  
pp. 22-44 ◽  
Author(s):  
Sangeeta Ramakrishnan ◽  
Xiaoqing Zhu ◽  
Frank Chan ◽  
Kashyap Kodanda Ram Kambhatla ◽  
Zheng Lu ◽  
...  
Keyword(s):  
Video Streaming ◽  
Bandwidth Allocation ◽  
Quality Of Experience ◽  
Video Quality ◽  
Bandwidth Management ◽  
Adaptive Video Streaming ◽  
Equal Rate ◽  
Optimized Allocation ◽  
Video Quality Monitoring

In this work, the authors present a novel bandwidth management solution for optimizing overall quality of experience (QoE) of multiple video streaming sessions. Instead of allocating bandwidth equally among competing flows, they propose to tailor the bandwidth allocation to both content complexity of requested video and playout buffer status of individual clients. The authors formulate the multi-client bandwidth allocation problem within the convex optimization framework, which is flexible enough to accommodate a wide variety of video quality metrics. Further, the authors present a practical architecture based on software defined networking (SDN) with two components: video quality monitoring and video quality optimization. Testbed-based experiments confirm that with quality-optimized allocation the network can support up to 75% more users at the same level of quality-of-experience (QoE) than conventional equal-rate allocations.

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