(5 versioni intermedie di uno stesso utente non sono mostrate) | |||
Riga 1: | Riga 1: | ||
− | =The project= | + | ==The project== |
− | + | A recent study by Cisco (2013) forecasts that the video will constitute more than 80% of Internet traffic in 2016. As a matter of facts, all the multimedia offer will be digitalized and made available through video streaming platform over the Internet. However, the implementation of a service that is able to scale to millions of concurrent viewers is an ongoing challenge. Cloud computing allows implementing scalable services both from the point of view of required resources to deliver a high-quality service and from the point of view of costs thanks to the pay-as-you-go billing model. | |
+ | However, to implement a seamless user experience while containing the distribution costs, it is necessary to build a control plane to efficiently manage the Cloud resources. | ||
+ | This research project proposes to apply the theory of feedback control to design control systems for 1) the management of the cloud resources and 2) the adaptation of the video bitrate to the available bandwidth and the user device. The expected results are: 1) a mathematical framework that allows modeling the delivery system including the clients; 2) algorithms to minimize the distribution costs while maximizing the user's quality of experience. The results will be made available in the form of a demonstrator. | ||
+ | The project results can be exploited through the integration of the proposed solutions in portals for the distribution of movies, short movies, independent productions, also through social platforms. | ||
+ | ==Publications== | ||
+ | <paper authors="Luca De Cicco, Saverio Mascolo, Vittorio Palmisano" conference="Ad Hoc Networks, Elsevier" place="vol. 89, pp. 170-176, 10.1016/j.adhoc.2019.02.008" date="1 June, 2019"> | ||
+ | QoE-driven Resource Allocation for Massive Video Distribution | ||
+ | </paper> | ||
+ | |||
+ | <paper authors="Luca De Cicco, Giuseppe Cilli, Saverio Mascolo" conference="10th ACM Multimedia Systems Conference (ACM MMSys '19)" date="June 18-21, 2019" place ="Amherst, MA, USA"> | ||
+ | ERUDITE: a Deep Neural Network for Optimal Tuning of Adaptive Video Streaming Controllers | ||
+ | </paper> | ||
+ | |||
+ | <paper authors="Giuseppe Ribezzo, Luca De Cicco, Vittorio Palmisano, Saverio Mascolo" conference="Proc. of Balkancom 2018" place="Podgorica, Montenegro" date="June 2018"> | ||
+ | Reducing Network Bandwidth Requirements for Immersive Video Streaming | ||
+ | </paper> | ||
+ | |||
+ | <paper authors="Giuseppe Ribezzo, Giuseppe Samela, Luca De Cicco, Vittorio Palmisano, Saverio Mascolo" conference="Proc. ACM Multimedia Systems Conference (ACM MMSYS) - Demo" place="Amsterdam, The Netherlands" date="June 2018"> | ||
+ | A DASH Video Streaming System for Immersive Contents | ||
+ | </paper> | ||
− | == | + | <paper authors="Giuseppe Cofano, Luca De Cicco, Saverio Mascolo" conference="IEEE Transactions on Control of Network Systems" place="vol. 5, no. 1, pp. 548-559, doi: 10.1109/TCNS.2016.2631452" date="March 2018" pdf='tcones-mavscs.pdf'> |
+ | Modeling and Design of Adaptive Video Streaming Control Systems | ||
+ | </paper> | ||
− | == | + | <paper authors="G. Cofano, L. De Cicco, T. Zinner, A. Nguyen-Ngoc, P. Tran-Gia, and S. Mascolo" conference="ACM Transaction on Multimedia Computing, Communications, and Applications (TOMM) (invited paper for the special issue best papers of the ACM Mmsys 2016 conference)" place="accepted, to appear" date="March 2017"> |
+ | Design and Experimental Evaluation of Network-assisted Control Strategies for HTTP Adaptive Video Streaming | ||
+ | </paper> | ||
+ | |||
+ | <paper authors="Giuseppe Cofano, Luca De Cicco, Saverio Mascolo" conference="Proc. of IEEE Conference on Decision and Control (IEEE CDC 2016)" place="Las Vegas, Nevada, USA" date="Dec 2016" pdf='cdc-16.pdf' slides='cdc-16-slides.pdf'> | ||
+ | A Hybrid Model of Adaptive Video Streaming Control Systems | ||
+ | </paper> |
A recent study by Cisco (2013) forecasts that the video will constitute more than 80% of Internet traffic in 2016. As a matter of facts, all the multimedia offer will be digitalized and made available through video streaming platform over the Internet. However, the implementation of a service that is able to scale to millions of concurrent viewers is an ongoing challenge. Cloud computing allows implementing scalable services both from the point of view of required resources to deliver a high-quality service and from the point of view of costs thanks to the pay-as-you-go billing model. However, to implement a seamless user experience while containing the distribution costs, it is necessary to build a control plane to efficiently manage the Cloud resources. This research project proposes to apply the theory of feedback control to design control systems for 1) the management of the cloud resources and 2) the adaptation of the video bitrate to the available bandwidth and the user device. The expected results are: 1) a mathematical framework that allows modeling the delivery system including the clients; 2) algorithms to minimize the distribution costs while maximizing the user's quality of experience. The results will be made available in the form of a demonstrator. The project results can be exploited through the integration of the proposed solutions in portals for the distribution of movies, short movies, independent productions, also through social platforms.