User-Oriented Video Streaming Service Based on Passive Aggressive Learning

2020 ◽  
pp. 491-511
Author(s):  
Makoto Oide ◽  
Akiko Takahashi ◽  
Toru Abe ◽  
Takuo Suganuma
Keyword(s):  
Video Streaming ◽  
Learning Algorithm ◽  
Service Provisioning ◽  
Network Resources ◽  
Regression Problem ◽  
Design And Implementation ◽  
Streaming Services ◽  
Streaming Service ◽  
Online Learning Algorithm

The authors propose a method to dynamically determine appropriate quality of service (QoS) required by users for video streaming services. In the proposed method, the video bit rate as the QoS parameter is determined based on the passive aggressive learning, which is an online learning algorithm for a regression problem, according to the user requirements, computational/network resources and service provisioning environments. Moreover, the method makes it possible to provide appropriate QoS by using optimization solution. In this paper, the authors describe the design and implementation of the method, then confirm the feasibility of the proposed method through the experiments.

User-Oriented Video Streaming Service Based on Passive Aggressive Learning

2017 ◽  
Vol 9 (1) ◽  
pp. 35-54 ◽  
Author(s):  
Makoto Oide ◽  
Akiko Takahashi ◽  
Toru Abe ◽  
Takuo Suganuma
Keyword(s):  
Video Streaming ◽  
Learning Algorithm ◽  
User Requirements ◽  
Service Provisioning ◽  
Network Resources ◽  
Regression Problem ◽  
Streaming Services ◽  
Streaming Service ◽  
Online Learning Algorithm

The authors propose a method to dynamically determine appropriate quality of service (QoS) required by users for video streaming services. In the proposed method, the video bit rate as the QoS parameter is determined based on the passive aggressive learning, which is an online learning algorithm for a regression problem, according to the user requirements, computational/network resources and service provisioning environments. Moreover, the method makes it possible to provide appropriate QoS by using optimization solution. In this paper, the authors describe the design and implementation of the method, then confirm the feasibility of the proposed method through the experiments.

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.

Simulation Study for the Performance Impacts of Video Multicast Caused by In-Cast in Cloud Computing Environment

2014 ◽  
Vol 598 ◽  
pp. 677-681
Author(s):  
Yen Wen Chen ◽  
Yuan Sing Ho ◽  
Chung Chen Sun
Keyword(s):  
Cloud Computing ◽  
Video Streaming ◽  
Packet Transmission ◽  
System Throughput ◽  
Transmission Performance ◽  
Software Environment ◽  
Cloud Computing Environment ◽  
Video Multicast ◽  
Streaming Services ◽  
Streaming Service

Cloud computing is becoming one of the most convenient ways for information services through network accesses. In this paper, we describe the architecture of cloud computing and discuss the issue of in-cast, which may downgrade the transmission performance when the number of simultaneous transmission servers is getting larger. The in-cast transmission may introduce the out of sequence of packet delivery and may reduce the system throughput. This paper constructs the software environment to simulate the performance of video streaming services delivered by the servers in cloud. We first examine the performance of the out of sequence issue of packet transmission when the Hashed Credit Fair (HCF) algorithm was applied and investigate the multicast performance of video streaming service with respect to the numbers of servers. With the increasing of the number of servers, we note that transmission performance is not improved, on the contrary, the system throughput downgrades sharply. We believe the simulation results of this paper will be helpful for the design of cloud video multicast services.

scholarly journals Video Packet Distribution Scheme for Multimedia Streaming Services in VANETs

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7368
Author(s):  
Yongje Shin ◽  
Hyunseok Choi ◽  
Youngju Nam ◽  
Euisin Lee
Keyword(s):  
Video Streaming ◽  
Streaming Data ◽  
Data Delivery ◽  
High Quality ◽  
Video Packet ◽  
Distribution Scheme ◽  
Quality Video ◽  
Streaming Services ◽  
High Quality Video

By leveraging the development of mobile communication technologies and due to the increased capabilities of mobile devices, mobile multimedia services have gained prominence for supporting high-quality video streaming services. In vehicular ad-hoc networks (VANETs), high-quality video streaming services are focused on providing safety and infotainment applications to vehicles on the roads. Video streaming data require elastic and continuous video packet distributions to vehicles to present interactive real-time views of meaningful scenarios on the road. However, the high mobility of vehicles is one of the fundamental and important challenging issues for video streaming services in VANETs. Nevertheless, previous studies neither dealt with suitable data caching for supporting the mobility of vehicles nor provided appropriate seamless packet forwarding for ensuring the quality of service (QoS) and quality of experience (QoE) of real-time video streaming services. To address this problem, this paper proposes a video packet distribution scheme named Clone, which integrates vehicle-to-vehicle and vehicle-to-infrastructure communications to disseminate video packets for video streaming services in VANETs. First, an indicator called current network quality information (CNQI) is defined to measure the feature of data forwarding of each node to its neighbor nodes in terms of data delivery ratio and delay. Based on the CNQI value of each node and the trajectory of the destination vehicle, access points called clones are selected to cache video data packets from data sources. Subsequently, packet distribution optimization is conducted to determine the number of video packets to cache in each clone. Finally, data delivery synchronization is established to support seamless streaming data delivery from a clone to the destination vehicle. The experimental results show that the proposed scheme achieves high-quality video streaming services in terms of QoS and QoE compared with existing schemes.

scholarly journals An Adaptive Neuro-Fuzzy Model for Quality Estimation in Wireless 2D/3D Video Streaming Systems

2019 ◽  
Vol 9 (2) ◽  
pp. 2291-2297
Keyword(s):  
Video Streaming ◽  
3D Video ◽  
Influential Factor ◽  
Estimation Accuracy ◽  
Estimation Model ◽  
Inference System ◽  
Experimental Development ◽  
Streaming Services ◽  
2D And 3D

Delivering high Quality of Experience (QoE) is essential to the success of today’s subscription for internet video streaming services. Quality of Service (QoS) metrics are considered by the research community as the most influential factor on video QoE. Therefore, establishing QoS-QoE correlation becomes critical for improving video QoE estimation. This paper presents experimental development of effective correlation between QoE and QoS for both 2D and 3D video streaming services. This is then used to build an objective QoE estimation model for real-time streaming of both 2D and 3D video contents over wireless networks. This model is based on using Adaptive Neural Fuzzy Inference System (ANFIS) to estimate the perceived video QoE. The proposed QoE model was trained with a set of media and packet layers’ metrics, taking into account the effect of video content type, dimension, and different packet loss metrics. The performance of the proposed QoE estimation model shows a considerable estimation accuracy with a correlation coefficient of 92% and 0.167 RMSE.

scholarly journals Analysis of Mobility Impacts on LTE Network for Video Streaming Services using Distributed Antenna System

2017 ◽  
Vol 1 (02) ◽  
pp. 79
Author(s):  
Hasanah Putri ◽  
Tri Nopiani Damayanti ◽  
Rohmat Tulloh
Keyword(s):  
Video Streaming ◽  
High Speed ◽  
Antenna System ◽  
User Mobility ◽  
Distributed Antenna System ◽  
Lte Network ◽  
Distributed Antenna ◽  
Wide Range ◽  
Streaming Services

LTE (Long Term Evolution) is a Broadband Wireless Access (BWA) technology that allows high speed and a wide range of access. LTE is designed to meet the needs for Quality of Service (QoS), i.e. the ability to download up to 300 Mbps and upload up to 75 Mbps. This study investigated the impacts of user mobility on the LTE network for video streaming services. The approach employed in this study included multi-user with Distributed AntennaSystem (DAS) and various variations of user mobility speed. Observations were made on the condition of the user moving from one cell to another so that the handover occurred. The throughput value will increase by 33% and 47% when the user’s distances are respectively 1250 m and 2000 m from eNode B. In addition, the delay value will reduce by 66.32% an 67.58% when the user’s distances are respectively 1250 m and 2000 m from eNode B. Moreover, the PDR value will increase by 48.74% and 55.45% when the user’s distances are respectively 1250 m and 2000 m from eNode B. The use of a distributed antenna system (DAS) model on LTE network has resulted in improved quality of performance when the user streams a video.

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 ).

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