Abstract
Multiparty videoconferences, or more generally multiparty video calls, are gaining a lot of popularity as they offer a rich communication experience. These applications have, however, large requirements in terms of both network and computational resources and have to deal with sets of heterogenous clients. The multiparty videoconferencing systems are usually either based on expensive central nodes, called Multipoint Control Units (MCU), with transcoding capabilities, or on a peer-to-peer architecture where users cooperate to distribute more efficiently the different video streams. Whereas the first class of systems requires an expensive central hardware, the second one depends completely on the redistribution capacity of the users, which sometimes might neither provide sufficient bandwidth nor be reliable enough. In this work, we propose an alternative solution where we use a central node to distribute the video streams, but at the same time we maintain the hardware complexity and the computational requirements of this node as low as possible, for example, it has no video decoding capabilities. We formulate the rate allocation problem as an optimization problem that aims at maximizing the Quality of Service (QoS) of the videoconference. We propose two different distributed algorithms for solving the optimization problem: the first algorithm is able to find an approximate solution of the problem in a one-shot execution, whereas the second algorithm, based on Lagrangian relaxation, performs iterative updates of the optimization variables in order to gradually increase the value of the objective function. The two algorithms, though being disjointed, nicely complement each other. If executed in sequence, they allow us to achieve both a quick approximate rate reallocation, in case of a sudden change of the system conditions, and a precise refinement of the variables, which avoids problems caused by possible faulty approximate solutions. We have further implemented our solution in a network simulator where we show that our rate allocation algorithm is able to properly optimize users’ QoS. We also illustrate the benefits of our solution in terms of network usage and overall utility when compared to a baseline heuristic method operating on the same system architecture.
- Cisco video and telepresence architecture design guide. Cisco (2012).Google Scholar
- RTP Media Congestion Avoidance Techniques (RMCAT). IETF Working Group (2012).Google Scholar
- The zettabyte era: Trends and analysis. White Paper, Cisco (2015).Google Scholar
- Stephen Boyd and Lieven Vandenberghe. 2004. Convex Optimization. Cambridge University Press. Google Scholar
Digital Library
- Samuel Burer and Adam N. Letchford. 2012. Non-convex mixed-integer nonlinear programming: A survey. Surv. Oper. Res. Manag. Sci. 17, 2 (2012).Google Scholar
- Minghua Chen, Miroslav Ponec, Sudipta Sengupta, Jin Li, and Philip A. Chou. 2008. Utility maximization in peer-to-peer systems. In Proceedings of the SIGMETRICS Performance Evaluation Review. ACM. Google Scholar
Digital Library
- Xiangwen Chen, Minghua Chen, Baochun Li, Yao Zhao, Yunnan Wu, and Jin Li. 2011. Celerity: A low-delay multi-party conferencing solution. In Proceedings of the International Conference on Multimedia. ACM. Google Scholar
Digital Library
- Douglas E. Comer. 2006. Internetworking with TCP/IP. Vol. 1. Pearson.Google Scholar
- Stefano D’Aronco, Sergio Mena, and Pascal Frossard. 2016. Distributed rate allocation in switch-based multiparty videoconference. In Proceedings of the 7th International Conference on Multimedia Systems. ACM. Google Scholar
Digital Library
- Alex Eleftheriadis. 2011. SVC and video communications. White Paper, Vydio (2011).Google Scholar
- Boris Grozev, Lyubomir Marinov, Varun Singh, and Emil Ivov. 2015. Last N: Relevance-based selectivity for forwarding video in multimedia conferences. In Proceedings of the 25th Workshop on Network and Operating Systems Support for Digital Audio and Video. ACM. Google Scholar
Digital Library
- Frank P. Kelly, Aman K. Maulloo, and David K. H. Tan. 1998. Rate control for communication networks: Shadow prices, proportional fairness and stability. J. Oper. Res. Soc. 49, 3 (1998).Google Scholar
Cross Ref
- Eymen Kurdoglu, Yong Liu, and Yao Wang. 2016. Dealing with user heterogeneity in P2P multi-party video conferencing: Layered distribution versus partitioned simulcast. IEEE Trans. Multimed. 18, 1 (2016).Google Scholar
Digital Library
- Jin Li, Philip A. Chou, and Cha Zhang. 2005. Mutualcast: An efficient mechanism for one-to-many content distribution. In Proceedings of the SIGCOMM Asia Workshop. ACM.Google Scholar
- Jiangchuan Liu, Bo Li, Y. T. Hou, and I. Chlamtac. 2004. On optimal layering and bandwidth allocation for multisession video broadcasting. IEEE Trans. Wireless Commun. 3, 2 (2004), 656--667. Google Scholar
Digital Library
- Daniel Pérez Palomar and Mung Chiang. 2006. A tutorial on decomposition methods for network utility maximization. IEEE J. Select. Areas Commun. 24, 8 (2006). Google Scholar
Digital Library
- Miroslav Ponec, Sudipta Sengupta, Minghua Chen, Jin Li, Philip Chou, and others. 2009. Multi-rate peer-to-peer video conferencing: A distributed approach using scalable coding. In Proceedings of the International Conference on Multimedia and Expo. IEEE. Google Scholar
Digital Library
- George F. Riley and Thomas R. Henderson. 2010. The ns-3 network simulator modeling and tools for network simulation. In Modeling and Tools for Network Simulation. Springer. Berlin.Google Scholar
- Kirill Sakhnov, Ekaterina Verteletskaya, and Boris Simak. 2009. Dynamical energy-based speech/silence detector for speech enhancement applications. In Proceedings of the World Congress on Engineering.Google Scholar
- Alexander Schrijver. 1986. Theory of Linear and Integer Programming. John Wiley 8 Sons, Inc. Google Scholar
Digital Library
- Heiko Schwarz, Detlev Marpe, and Thomas Wiegand. 2007. Overview of the scalable video coding extension of the H. 264/AVC standard. IEEE Trans. Circ. Syst. Video Technol. 17, 9 (2007). Google Scholar
Digital Library
- M. Welzl, S. Islam, and S. Gjessing. 2015. Coupled congestion control for RTP media. Internet draft (2015). https://tools.ietf.org/html/draft-welzl-rmcat-coupled-cc.Google Scholar
- Yang Xu, Chenguang Yu, Jingjiang Li, and Yong Liu. 2012. Video telephony for end-consumers: Measurement study of google+, iChat, and skype. In Proceedings of the Conference on Internet Measurement Conference. ACM. Google Scholar
Digital Library
- Yang Richard Yang, Min Sik Kim, and Simon S. Lam. 2000. Optimal partitioning of multicast receivers. In Proceedings of the International Conference on Network Protocols. IEEE. Google Scholar
Digital Library
- Xiaoqing Zhu and others. 2015. NADA: A Unified Congestion Control Scheme for Real-Time Media. Internet draft (2015). https://tools.ietf.org/html/draft-zhu-rmcat-nada.Google Scholar
Index Terms
Distributed Rate Allocation in Switch-Based Multiparty Videoconferencing System
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