scholarly journals Multi-Session Multicasting for 360-Degree Video Multicast over OFDMA Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Jounsup Park
Keyword(s):  
User Experience ◽  
Mobile Networks ◽  
Experimental Results ◽  
Video Content ◽  
Video Multicast ◽  
Limited Bandwidth ◽  
Sports Events ◽  
Wireless Spectrum

360-degree video content provides a rich and immersive multimedia experience to viewers by allowing viewers to the video from any angle. However, 360-degree videos require much higher bandwidth to be delivered over mobile networks compared to conventional videos. Multicasting of the videos is one of the solutions to efficiently utilize the limited bandwidth since many viewers share the wireless spectrum resource for popular videos, such as sports events or musical concerts. LTE eMBMS assigns the videos to the video sessions, and multiple viewers can subscribe to the same video allocated to the video sessions. Moreover, the tiling of the 360-degree video makes it possible to control the regional quality of the video. The tiles that are likely to be seen by many viewers should have higher quality than other tiles to satisfy more viewers. In this paper, we proposed the Multi-Session Multicast (MSM) system to optimally allocate the wireless resources to tiles with different qualities to maximize the expected user experience. The experimental results show that the proposed MSM system provides higher quality videos to viewers using limited wireless resources.

Adaptive Retransmission Scheme for Video Streaming Applications

2010 ◽  
pp. 248-262
Author(s):  
Árpád Huszák
Keyword(s):  
Video Streaming ◽  
Mobile Networks ◽  
Video Quality ◽  
Multimedia Applications ◽  
Limited Bandwidth ◽  
Streaming Applications ◽  
Congestion Control Algorithm ◽  
Negative Effect ◽  
Audio Video

In this chapter we present a novel selective retransmission scheme, based on congestion control algorithm. Our method is efficient in narrowband networks for multimedia applications, which demand higher bandwidth. Multimedia applications are becoming increasingly popular in IP networks, while in mobile networks the limited bandwidth and the higher error rate arise in spite of its popularity. These are restraining factors for mobile clients using multimedia applications such as video streaming. In some conditions the retransmission of lost and corrupted packets should increase the quality of the multimedia service, but these retransmissions should be enabled only if the network is not in congested state. Otherwise the retransmitted packet will intensify the congestion and it will have negative effect on the audio/video quality. Our proposed mechanism selectively retransmits the corrupted packets based on the actual video bit rate and the TCP-Friendly Rate Control (TFRC), which is integrated to the preferred DCCP transport protocol.

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 Data-Driven Construction of User Utility Functions from Radio Connection Traces in LTE

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 829
Author(s):  
Antonio J. García ◽  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez
Keyword(s):  
User Experience ◽  
Mobile Networks ◽  
Network Performance ◽  
Packet Delay ◽  
Lte Networks ◽  
Management Processes ◽  
Adequate Quality ◽  
Qoe Model ◽  
The Impact

In recent years, the number of services in mobile networks has increased exponentially. This increase has forced operators to change their network management processes to ensure an adequate Quality of Experience (QoE). A key component in QoE management is the availability of a precise QoE model for every service that reflects the impact of network performance variations on the end-user experience. In this work, an automatic method is presented for deriving Quality-of-Service (QoS) thresholds in analytical QoE models of several services from radio connection traces collected in an Long Term Evolution (LTE) network. Such QoS thresholds reflect the minimum connection performance below which a user gives up its connection. The proposed method relies on the fact that user experience influences the traffic volume requested by users. Method assessment is performed with real connection traces taken from live LTE networks. Results confirm that packet delay or user throughput are critical factors for user experience in the analyzed services.

scholarly journals Video Captioning with Tube Features

2018 ◽  
Author(s):  
Bin Zhao ◽  
Xuelong Li ◽  
Xiaoqiang Lu
Keyword(s):  
Experimental Results ◽  
Visual Feature ◽  
Video Content ◽  
Dynamic Information ◽  
Video Captioning ◽  
Attention Model ◽  
Decoder Architecture ◽  
Benchmark Datasets

Visual feature plays an important role in the video captioning task. Considering that the video content is mainly composed of the activities of salient objects, it has restricted the caption quality of current approaches which just focus on global frame features while paying less attention to the salient objects. To tackle this problem, in this paper, we design an object-aware feature for video captioning, denoted as tube feature. Firstly, Faster-RCNN is employed to extract object regions in frames, and a tube generation method is developed to connect the regions from different frames but belonging to the same object. After that, an encoder-decoder architecture is constructed for video caption generation. Specifically, the encoder is a bi-directional LSTM, which is utilized to capture the dynamic information of each tube. The decoder is a single LSTM extended with an attention model, which enables our approach to adaptively attend to the most correlated tubes when generating the caption. We evaluate our approach on two benchmark datasets: MSVD and Charades. The experimental results have demonstrated the effectiveness of tube feature in the video captioning task.

Detecting “DeepFakes” in H.264 Video Data Using Compression Ghost Artifacts

2020 ◽  
Vol 2020 (4) ◽  
pp. 116-1-116-7
Author(s):  
Raphael Antonius Frick ◽  
Sascha Zmudzinski ◽  
Martin Steinebach
Keyword(s):  
Image Forensics ◽  
Video Data ◽  
Experimental Results ◽  
Video Sequences ◽  
The Internet ◽  
Video Content ◽  
High Quality ◽  
The Public

In recent years, the number of forged videos circulating on the Internet has immensely increased. Software and services to create such forgeries have become more and more accessible to the public. In this regard, the risk of malicious use of forged videos has risen. This work proposes an approach based on the Ghost effect knwon from image forensics for detecting forgeries in videos that can replace faces in video sequences or change the mimic of a face. The experimental results show that the proposed approach is able to identify forgery in high-quality encoded video content.

Introduction to Games User Research

2018 ◽  
Author(s):  
Anders Drachen ◽  
Pejman Mirza-Babaei ◽  
Lennart E. Nacke
Keyword(s):  
User Experience ◽  
Video Game ◽  
Digital Games ◽  
Comprehensive Overview ◽  
User Research ◽  
Interdisciplinary Field ◽  
Present Book ◽  
Optimal Quality ◽  
Experienced User

This chapter provides an introduction to the field of Games User Research (GUR) and to the present book. GUR is an interdisciplinary field of practice and research concerned with ensuring the optimal quality of usability and user experience in digital games. GUR inevitably involves any aspect of a video game that players interface with, directly or indirectly. This book aims to provide the foundational, accessible, go-to resource for people interested in GUR. It is a community-driven effort—it is written by passionate professionals and researchers in the GUR community as a handbook and guide for everyone interested in user research and games. We aim to provide the most comprehensive overview from an applied perspective, for a person new to GUR, but which is also useful for experienced user researchers.

scholarly journals A quality analysis of the YouTube video content on meniscus repair surgery

2021 ◽  
Vol 9 (2_suppl) ◽  
pp. 2325967121S0001
Author(s):  
François Sigonney ◽  
Camille Steltzlen ◽  
Pierre Alban Bouché ◽  
Nicolas Pujol
Keyword(s):  
Medical Information ◽  
Cruciate Ligament ◽  
Meniscus Repair ◽  
Quality Analysis ◽  
Video Content ◽  
Source Type ◽  
Keywords Meniscus ◽  
Youtube Videos ◽  
Anterior Cruciate

Objectives: The Internet, especially YouTube, is an important and growing source of medical information. The content of this information is poorly evaluated. The objective of this study was to analyze the quality of YouTube video content on meniscus repair. The hypothesis was that this source of information is not relevant for patients. Methods: A YouTube search was carried out using the keywords "meniscus repair". Videos had to have had more than 10,000 views to be included. The videos were analyzed by two evaluators. Various features of the videos were recorded (number of views, date of publication, "likes", "don’t likes", number of comments, source, type of content and the origin of the video). The quality of the video content was analyzed by two validated information system scores: the JAMA benchmark score (0 to 4) and the Modified DISCERN score (0 to 5). A specific meniscus repair score (MRSS scored out of 22) was developed for this study, in the same way that a specific score has been developed for other similar studies (anterior cruciate ligament, spine, etc.). Results: Forty-four (44) videos were included in the study. The average number of views per video was 180,100 (± 222,000) for a total number of views of 7,924,095. The majority of the videos were from North America (90.9%). In most cases, the source (uploader) that published the video was a doctor (59.1%). A manufacturer, an institution and a non-medical source were the other sources. The content actually contained information on meniscus repair in only 50% of the cases. The mean scores for the JAMA benchmark, MD score and MRSS were 1.6/4± 0.75, 1.2/5 ± 1.02 and 4.5/22 (± 4.01) respectively. No correlation was found between the number of views and the quality of the videos. The quality of videos from medical sources was not superior to those from other sources. Conclusion: The content of YouTube videos on meniscus repair is of very low quality. Physicians should inform patients and, more importantly, contribute to the improvement of these contents.

Hybrid features prediction model of movie quality using Multi-machine learning techniques for effective business resource planning

2021 ◽  
Vol 40 (5) ◽  
pp. 9361-9382 ◽  
Author(s):  
Naeem Iqbal ◽  
Rashid Ahmad ◽  
Faisal Jamil ◽  
Do-Hyeun Kim
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Resource Planning ◽  
Experimental Results ◽  
Quality Prediction ◽  
Classification Models ◽  
Hybrid Features ◽  
Social Media Data ◽  
Media Data

Quality prediction plays an essential role in the business outcome of the product. Due to the business interest of the concept, it has extensively been studied in the last few years. Advancement in machine learning (ML) techniques and with the advent of robust and sophisticated ML algorithms, it is required to analyze the factors influencing the success of the movies. This paper presents a hybrid features prediction model based on pre-released and social media data features using multiple ML techniques to predict the quality of the pre-released movies for effective business resource planning. This study aims to integrate pre-released and social media data features to form a hybrid features-based movie quality prediction (MQP) model. The proposed model comprises of two different experimental models; (i) predict movies quality using the original set of features and (ii) develop a subset of features based on principle component analysis technique to predict movies success class. This work employ and implement different ML-based classification models, such as Decision Tree (DT), Support Vector Machines with the linear and quadratic kernel (L-SVM and Q-SVM), Logistic Regression (LR), Bagged Tree (BT) and Boosted Tree (BOT), to predict the quality of the movies. Different performance measures are utilized to evaluate the performance of the proposed ML-based classification models, such as Accuracy (AC), Precision (PR), Recall (RE), and F-Measure (FM). The experimental results reveal that BT and BOT classifiers performed accurately and produced high accuracy compared to other classifiers, such as DT, LR, LSVM, and Q-SVM. The BT and BOT classifiers achieved an accuracy of 90.1% and 89.7%, which shows an efficiency of the proposed MQP model compared to other state-of-art- techniques. The proposed work is also compared with existing prediction models, and experimental results indicate that the proposed MQP model performed slightly better compared to other models. The experimental results will help the movies industry to formulate business resources effectively, such as investment, number of screens, and release date planning, etc.

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