Efficient Transcoding and Streaming Mechanism in Multiple Cloud Domains

Mada, Badr Eddine; Bagaa, Miloud; Taleb, Tarik

doi:10.1109/glocom.2017.8254123

Cited by 6 publications

(2 citation statements)

References 24 publications

(20 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• Secure transcoder: this component allows the transcoding of the uploaded videos to different resolutions using softwares such as FFMPEG. Each resolution is subdivided into small chunks of fixed time duration [35]. After the transcoding operation ends, the secure transcoder creates a hash for each chunk and sends that hash to the video integrity checker module (VICM).…”

Section: B Secure Videos Module (Svm)mentioning

confidence: 99%

Trust-Based Video Management Framework for Social Multimedia Networks

Mada

Bagaa

Taleb

2019

IEEE Trans. Multimedia

Self Cite

View full text Add to dashboard Cite

Social Multimedia Networks (SMNs) have attracted much attention from both academia and industry due to their impact on our daily lives. The requirements of SMN users are increasing along with time, which make the satisfaction of those requirements a very challenging process. One important challenge facing SMNs consists of their internal users that can upload and manipulate insecure, untrusted and unauthorized contents. For this purpose, controlling and verifying content delivered to end-users is becoming a highly challenging process. So far, many researchers have investigated the possibilities of implementing a trustworthy SMN. In this vein, the aim of this paper is to propose a framework that allows collaboration between humans and machines to ensure secure delivery of trusted videos content over SMNs while ensuring an optimal deployment cost in the form of CPU, RAM, and storage. The key concepts beneath the proposed framework consist in i) assigning to each user a level of trust based on his/her history, ii) creating an intelligent agent that decides which content can be automatically published on the network and which content should be reviewed or rejected, and iii) checking the videos' integrity and delivery during the streaming process. Accordingly, we ensure that the trust level of the SMNs increases. Simultaneously, efficient Capital Expenditure (CAPEX) and Operational Expenditures (OPEX) can be achieved. Index Terms-Social multimedia network, video streaming, trust model, and trust management. • Secure storage: this component mainly works as follows: first of all, an authorized user sends an upload request

show abstract

Section: B Secure Videos Module (Svm)mentioning

confidence: 99%

Trust-Based Video Management Framework for Social Multimedia Networks

Mada

Bagaa

Taleb

2019

IEEE Trans. Multimedia

Self Cite

View full text Add to dashboard Cite

show abstract

“…In recent years, video content accounts for a large proportion of global Internet consumption. Video steaming is gradually becoming the most attractive service [1][2][3]. However, Internet does not provide any quality of service guarantees to video content delivery.…”

Section: Introductionmentioning

confidence: 99%

QoS Prediction for Neighbor Selection via Deep Transfer Collaborative Filtering in Video Streaming P2P Networks

Zhang

et al. 2019

International Journal of Digital Multimedia Broadcasting

View full text Add to dashboard Cite

To expand the server capacity and reduce the bandwidth, P2P technologies are widely used in video streaming systems in recent years. Each client in the P2P streaming network should select a group of neighbors by evaluating the QoS of the other nodes. Unfortunately, the size of video streaming P2P network is usually very large, and evaluating the QoS of all the other nodes is resource-consuming. An attractive way is that we can predict the QoS of a node by taking advantage of the past usage experiences of a small number of the other clients who have evaluated this node. Therefore, collaborative filtering (CF) methods could be used for QoS evaluation to select neighbors. However, we might use different QoS properties for different video streaming policies. If a new video steaming policy needs to evaluate a new QoS property, but the historical experiences include very few evaluation data for this QoS property, CF methods would incur severe overfitting issues, and the clients then might get unsatisfied recommendation results. In this paper, we proposed a novel neural collaborative filtering method based on transfer learning, which can evaluate the QoS with few historical data by evaluating the other different QoS properties with rich historical data. We conduct our experiments on a large real-world dataset, the QoS values of which are obtained from 339 clients evaluating on the other 5825 clients. The comprehensive experimental studies show that our approach offers higher prediction accuracy than the traditional collaborative filtering approaches.

show abstract