Smart resource allocation for improving QoE in IP Multimedia Subsystems

[EN] IP Multimedia Subsystem (IMS) is a robust multimedia service. IMS becomes more important when delivering multimedia services. Multimedia service providers can benefit from IMS to ensure a good QoE (Quality of Experience) to their customers with minimal resources usage. In this paper, we propose an intelligent media distribution IMS system architecture for delivering video streaming. The system is based primarily on uploading a multimedia file to a server in the IMS. Later, other users can download the uploaded multimedia file from the IMS. In the system, we also provide the design of the heuristic decision methods and models based on probability distributions. Thus, our system takes into account the network parameters such as bandwidth, jitter, delay and packet loss that influence the QoE of the end -users. Moreover, we have considered the other parameters of the energy consumption such as CPU, RAM, temperature and number connected users that impact the result of the QoE. All these parameters are considered as input to our proposal management system. The measurements taken from the real test bench show the real performance and demonstrate the success of the system about ensuring the upload speed of the multimedia file, guaranteeing the QoE of end users and improving the energy efficiency of the IMS.This work has been partially supported by the "Ministerio de Ciencia e Innovation", through the "Plan Nacional de I+D+i 2008-2011" in the "Subprograma de Proyectos de Investigation Fundamental", project TEC2011-27516, and by the Polytechnic University of Valencia, though the PAID-15-11 multidisciplinary projects.Canovas Solbes, A.; Taha, M.; Lloret, J.; Tomás Gironés, J. (2018). Smart resource allocation for improving QoE in IP Multimedia Subsystems. Journal of Network and Computer Applications. 104:107-116. https://doi.org/10.1016/j.jnca.2017.12.020S10711610

Intelligent Algorithm for Enhancing MPEG-DASH QoE in eMBMS

[EN] Multimedia streaming is the most demanding and bandwidth hungry application in today¿s world of Internet. MPEG-DASH as a video technology standard is designed for delivering live or on-demand streams in Internet to deliver best quality content with the fewest dropouts and least possible buffering. Hybrid architecture of DASH and eMBMS has attracted a great attention from the telecommunication industry and multimedia services. It is deployed in response to the immense demand in multimedia traffic. However, handover and limited available resources of the system affected on dropping segments of the adaptive video streaming in eMBMS and it creates an adverse impact on Quality of Experience (QoE), which is creating trouble for service providers and network providers towards delivering the service. In this paper, we derive a case study in eMBMS to approach to provide test measures evaluating MPEG-DASH QoE, by defining the metrics are influenced on QoE in eMBMS such as bandwidth and packet loss then we observe the objective metrics like stalling (number, duration and place), buffer length and accumulative video time. Moreover, we build a smart algorithm to predict rate of segments are lost in multicast adaptive video streaming. The algorithm deploys an estimation decision regards how to recover the lost segments. According to the obtained results based on our proposal algorithm, rate of lost segments is highly decreased by comparing to the traditional approach of MPEG-DASH multicast and unicast for high number of users.This work has been partially supported by the Postdoctoral Scholarship Contratos Postdoctorales UPV 2014 (PAID-10-14) of the Universitat Politècnica de València , by the Programa para la Formación de Personal Investigador (FPI-2015-S2-884) of the Universitat Politècnica de València , by the Ministerio de Economía y Competitividad , through the Convocatoria 2014. Proyectos I+D - Programa Estatal de Investigación Científica y Técnica de Excelencia in the Subprograma Estatal de Generación de Conocimiento , project TIN2014-57991-C3-1-P and through the Convocatoria 2017 - Proyectos I+D+I - Programa Estatal de Investigación, Desarrollo e Innovación, convocatoria excelencia (Project TIN2017-84802-C2-1-P).Abdullah, MT.; Jimenez, JM.; Canovas Solbes, A.; Lloret, J. (2017). Intelligent Algorithm for Enhancing MPEG-DASH QoE in eMBMS. Network Protocols and Algorithms. 9(3-4):94-114. https://doi.org/10.5296/npa.v9i3-4.12573S9411493-

Multimedia Data Flow Traffic Classification Using Intelligent Models Based on Traffic Patterns

[EN] Nowadays, there is high interest in modeling the type of multimedia traffic with the purpose of estimating the network resources required to guarantee the quality delivered to the user. In this work we propose a multimedia traffic classification model based on patterns that allows us to differentiate the type of traffic by using video streaming and network characteristics as input parameters. We show that there is low correlation between network parameters and the delivered video quality. Because of this, in addition to network parameters, we also add video streaming parameters in order to improve the efficiency of our system. Finally, it should be noted that, based on the objective video quality received by the user, we have extracted traffic patterns that we use to perform the development of the classification model.This work has been supported by the Ministerio de Economia y Competitividad in the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento within the Project with reference TIN2017-84802-C2-1-P.Canovas Solbes, A.; Jimenez, JM.; Romero Martínez, JO.; Lloret, J. (2018). Multimedia Data Flow Traffic Classification Using Intelligent Models Based on Traffic Patterns. IEEE Network. 32(6):100-107. doi:10.1109/MNET.2018.180012110010732

A robust multimedia traffic SDN-Based management system using patterns and models of QoE estimation with BRNN

[EN] Nowadays, network infrastructures such as Software Defined Networks (SDN) achieve a huge computational power. This allows to add a high processing on the network nodes. In this paper, a multimedia traffic management system is presented. This system is based on estimation models of Quality of Experience (QoE) and also on the traffic patterns classification. In order to achieve this, a QoE estimation method has been modeled. This method allows for classifying the multimedia traffic from multimedia transmission patterns. In order to do this, the SDN controller gathers statistics from the network. The patterns used have been defined from a lineal combination of objective QoE measurements. The model has been defined by Bayesian regularized neural networks (BRNN). From this model, the system is able to classify several kind of traffic according to the quality perceived by the users. Then, a model has been developed to determine which video characteristics need to be changed to provide the user with the best possible quality in the critical moments of the transmission. The choice of these characteristics is based on the quality of service (QoS) parameters, such as delay, jitter, loss rate and bandwidth. Moreover, it is also based on subpatterns defined by clusters from the dataset and which represents network and video characteristics. When a critical network situation is given, the model selects, by using network parameters as entries, the subpattern with the most similar network condition. The minimum Euclidean distance between these entries and the network parameters of the subpatters is calculated to perform this selection. Both models work together to build a reliable multimedia traffic management system perfectly integrated into current network infrastructures, which is able to classify the traffic and solve critical situations changing the video characteristics, by using the SDN architecture.This work has been partially supported by the "Ministerio de Educacion, Cultura y Deporte", through the "Ayudas para contratos predoctorales de Formation del Profesorado Universitario FPU (Convocatoria 2015)", grant number FPU15/06837 and by the "Ministerio de Economia y Competitividad" in the "Programa Estatal de Fomento de la Investigation Cientffica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento" within the project under Grant TIN2017-84802-C2-1-P.Canovas Solbes, A.; Rego Mañez, A.; Romero Martínez, JO.; Lloret, J. (2020). A robust multimedia traffic SDN-Based management system using patterns and models of QoE estimation with BRNN. Journal of Network and Computer Applications. 150:1-14. https://doi.org/10.1016/j.jnca.2019.102498S114150Cánovas, A., Taha, M., Lloret, J., & Tomás, J. (2018). Smart resource allocation for improving QoE in IP Multimedia Subsystems. Journal of Network and Computer Applications, 104, 107-116. doi:10.1016/j.jnca.2017.12.020Canovas, A., Jimenez, J. M., Romero, O., & Lloret, J. (2018). Multimedia Data Flow Traffic Classification Using Intelligent Models Based on Traffic Patterns. IEEE Network, 32(6), 100-107. doi:10.1109/mnet.2018.1800121Burden, F., & Winkler, D. (2008). Bayesian Regularization of Neural Networks. Artificial Neural Networks, 23-42. doi:10.1007/978-1-60327-101-1_3Goodman, S. N. (2005). Introduction to Bayesian methods I: measuring the strength of evidence. Clinical Trials, 2(4), 282-290. doi:10.1191/1740774505cn098oaHirschen, K., & Schäfer, M. (2006). Bayesian regularization neural networks for optimizing fluid flow processes. Computer Methods in Applied Mechanics and Engineering, 195(7-8), 481-500. doi:10.1016/j.cma.2005.01.015Huang, X., Yuan, T., Qiao, G., & Ren, Y. (2018). Deep Reinforcement Learning for Multimedia Traffic Control in Software Defined Networking. IEEE Network, 32(6), 35-41. doi:10.1109/mnet.2018.1800097Lin, Y. (2002). Data Mining and Knowledge Discovery, 6(3), 259-275. doi:10.1023/a:1015469627679Lopez-Martin, M., Carro, B., Lloret, J., Egea, S., & Sanchez-Esguevillas, A. (2018). Deep Learning Model for Multimedia Quality of Experience Prediction Based on Network Flow Packets. IEEE Communications Magazine, 56(9), 110-117. doi:10.1109/mcom.2018.1701156Hagan, M. T., & Menhaj, M. B. (1994). Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks, 5(6), 989-993. doi:10.1109/72.329697Nguyen, T. T. T., & Armitage, G. (2008). A survey of techniques for internet traffic classification using machine learning. IEEE Communications Surveys & Tutorials, 10(4), 56-76. doi:10.1109/surv.2008.080406Queiroz, W., Capretz, M. A. M., & Dantas, M. (2019). An approach for SDN traffic monitoring based on big data techniques. Journal of Network and Computer Applications, 131, 28-39. doi:10.1016/j.jnca.2019.01.016Rego, A., Canovas, A., Jimenez, J. M., & Lloret, J. (2018). An Intelligent System for Video Surveillance in IoT Environments. IEEE Access, 6, 31580-31598. doi:10.1109/access.2018.2842034Seshadrinathan, K., Soundararajan, R., Bovik, A. C., & Cormack, L. K. (2010). Study of Subjective and Objective Quality Assessment of Video. IEEE Transactions on Image Processing, 19(6), 1427-1441. doi:10.1109/tip.2010.2042111Soysal, M., & Schmidt, E. G. (2010). Machine learning algorithms for accurate flow-based network traffic classification: Evaluation and comparison. Performance Evaluation, 67(6), 451-467. doi:10.1016/j.peva.2010.01.001Tan, X., Xie, Y., Ma, H., Yu, S., & Hu, J. (2019). Recognizing the content types of network traffic based on a hybrid DNN-HMM model. Journal of Network and Computer Applications, 142, 51-62. doi:10.1016/j.jnca.2019.06.004Tongaonkar, A., Torres, R., Iliofotou, M., Keralapura, R., & Nucci, A. (2015). Towards self adaptive network traffic classification. Computer Communications, 56, 35-46. doi:10.1016/j.comcom.2014.03.02

An Intelligent System for Video Surveillance in IoT Environments

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.[EN] Multimedia traffic has drastically grown in the last few years. In addition, some of the last paradigms proposed, like the Internet of Things (IoT), adds new types of traffic and applications. Software-defined networks (SDNs) improve the capability of network management. Combined with SDN, artificial intelligence (AI) can provide solutions to network problems based on classification and estimation techniques. In this paper, we propose an artificial intelligence system for detecting and correcting errors in multimedia transmission in a surveillance IoT environment connected through a SDN. The architecture, algorithm, and messages of the SDN are detailed. The AI system design is described, and the test-bed and the data set are explained. The AI module consists of two different parts. The first one is a classifying part, which detects the type of traffic that is sent through the network. The second part is an estimator that informs the SDN controller on which kind of action should be executed to guarantee the quality of service and quality of experience. Results show that with the actions performed by the network, like jitter can be reduced up to 70% of average and losses can be reduced from 9.07% to nearly 1.16%. Moreover, the presented AI module is able to detect critical traffic with 77% accuracyThis work was supported in part by the Ministerio de Educacion, Cultura y Deporte, through the Ayudas para contratos predoctorales de Formacion del Profesorado Universitario FPU (Convocatoria 2015) under Grant FPU15/06837, in part by the Programa para la Formacion de Personal Investigador de la Universitat Politecnica de Valencia 2014, Subprograma 2, (Codigo del contrato: 884), and in part by the Ministerio de Economia y Competitividad in the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento within the project under Grant TIN2014-57991-C3-1-P and Grant TIN2017-84802-C2-1-P.Rego Mañez, A.; Canovas Solbes, A.; Jimenez, JM.; Lloret, J. (2018). An Intelligent System for Video Surveillance in IoT Environments. IEEE Access. 6:31580-31598. https://doi.org/10.1109/ACCESS.2018.2842034S3158031598

A cognitive network management system to improve QoE in stereoscopic IPTV service

[EN] New Internet Protocol Television (IPTV) services are including new technologies such as Stereoscopic TV and three-dimensions (3D) HDTV. As well, increased ubiquitous networking and promoting in smart devices have led to high demand IPTV (over networks). Stereoscopic content required higher data flow to support these emerging TV services, and there are higher requirements at the network layer to provide good quality of service and quality of experience to the end users in delivering stereoscopic IPTV. In this paper, we propose a new concept of cognitive network management algorithm and protocol based on 3D coding techniques for delivering of stereoscopic IPTV service. The proposed approach explains how the management algorithm observes the network performance to guarantee the quality of the stereoscopic IPTV services, by measuring the performance of quality of service (QoS) parameters (delay, jitter, and packets loss) and quality of experience (QoE) metrics such as Peak Signal-to-Noise Ratio (PSNR), Moving Image Videography (MIV), and Mean Opinion Score (MOS). Those parameters are monitored in order to take appropriate codification decision for IPTV service provider. Moreover, the codification decision uses K-mean classification to select the better codification for the end users. Therefore, both kinds of 3D coding formats such as Stereo Video Coding (SVC) format and 2D + Z Coding format (2D-plus-Depth) are selected in our experiments. As a result, our proposal successfully ensures the appropriate quality of service and quality of experience to the end users when the service of stereoscopic IPTV is being delivered.Polytechnic University of Valencia, Grant/Award Number: PAID-15-11; Ministerio de Ciencia e Innovacion, Grant/Award Number: TEC2011-27516Canovas Solbes, A.; Taha, M.; Lloret, J.; Tomás Gironés, J. (2019). A cognitive network management system to improve QoE in stereoscopic IPTV service. International Journal of Communication Systems. 32(12):1-23. https://doi.org/10.1002/dac.3992S1233212Cruz, R. A. S., Nunes, M. S., Menezes, L., & Domingues, J. (2010). IPTV architecture for an IMS environment with dynamic QoS adaptation. Multimedia Tools and Applications, 53(3), 557-589. doi:10.1007/s11042-010-0537-8Arnaud, J., Négru, D., Sidibé, M., Pauty, J., & Koumaras, H. (2010). Adaptive IPTV services based on a novel IP Multimedia Subsystem. 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Adaptive Streaming: A subjective catalog to assess the performance of objective QoE metrics. Network Protocols and Algorithms, 6(2), 123. doi:10.5296/npa.v6i2.5461Hewage CTER Karim HA Worrall S Dogan S Kondoz AM Comparison of stereo video coding support in MPEG-4 MAC, H.264/AVC and H.264/SVC 2007Ohm JR Stereo/multiview encoding using the MPEG family of standards 1999Chu, C.-H. (2015). Visual Comfort for Stereoscopic 3D by Using Motion Sensors on 3D Mobile Devices. ACM Transactions on Multimedia Computing, Communications, and Applications, 12(1s), 1-20. doi:10.1145/2808211Calagari, K., Elgamal, T., Diab, K., Templin, K., Didyk, P., Matusik, W., & Hefeeda, M. (2016). Depth Personalization and Streaming of Stereoscopic Sports Videos. 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