NC Node Selection Game in Collaborative Streaming Systems

Pascal Frossard, Eymen Kurdoglu, Nikolaos Thomos
2010
Article de conférence

Network coding has been recently proposed as an efficient method to improve throughput, minimize delays and remove the need for reconciliation between network nodes in distributed streaming systems. It permits to take advantage of the path and node diversity in the network when the network coding nodes are placed efficiently. In this paper, we investigate networks consisting of nodes that autonomously determine whether they should perform network coding or not as well as their set of parent nodes. Each node makes its decisions that maximize its quality of service. The decisions include the selection of operation mode (i.e., network coding mode, simple data forwarding mode) and the selection of extra connections. The resulting interactions among the nodes are modeled as a congestion game, thereby ensuring an equilibrium, i.e., stable multimedia stream flow. The experimental results show that the proposed scheme is appropriate for distributed multimedia transmission since it provides a stable quality without imposing centralized control.

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Pascal Frossard, Eymen Kurdoglu, Nikolaos Thomos
2010
Article de conférence

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https://infoscience.epfl.ch/record/142857?ln=fr

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Collaborative video streaming with Raptor network coding

Pascal Frossard, Nikolaos Thomos

We investigate the problem of collaborative video streaming with Raptor network coding over overlay networks. We exploit path and source diversity, as well as basic processing capabilities of network nodes to increase the overall throughput and improve the video quality at the clients. We consider an architecture where several streaming servers simultaneously deliver video information to a set of clients. The servers apply Raptor coding on the video packets for error resiliency, and the forwarding peer nodes further combine the Raptor coded video packets in order to increase the packet diversity in the network. We find the optimal source and channel rate allocation in such a collaborative streaming system. The resulting scheme efficiently exploits the available network resources for improved video quality. The experimental evaluation demonstrates that it typically outperforms Raptor video streaming systems that do not use network coding.

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Selection of Network Coding Nodes for Minimal Playback Delay in Streaming Overlays

Nicolae Cleju, Pascal Frossard, Nikolaos Thomos

Network coding permits to deploy distributed packet delivery algorithms that locally adapt to the network availability in media streaming applications. However, it may also increase delay and computational complexity if it is not implemented efficiently. We address here the effective placement of nodes that implement randomized network coding in overlay networks, so that the goodput is kept high while the delay for decoding stays small in streaming applications. We first estimate the decoding delay at each client, which depends on the innovative rate in the network. This estimation permits to identify the nodes that have to perform coding for a reduced decoding delay. We then propose two iterative algorithms for selecting the nodes that should perform network coding. The first algorithm relies on the knowledge of the full network statistics. The second algorithm uses only local network statistics at each node. Simulation results show that large performance gains can be achieved with the selection of only a few network coding nodes. Moreover, the second algorithm performs very closely to the central estimation strategy, which demonstrates that the network coding nodes can be selected efficiently in a distributed manner. Our scheme shows large gains in terms of achieved throughput, delay and video quality in realistic overlay networks when compared to methods that employ traditional streaming strategies as well as random network nodes selection algorithms.

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Dynamic Perception after Visually Guided Grasping by a Human-Like Autonomous Robot

Kuniaki Noda, Mototaka Suzuki

We will explore dynamic perception following the visually guided grasping of several objects by a human-like autonomous robot. This competency serves for object categorization. Physical interaction with the hand-held object gives the neural network of the robot the rich, coherent and multi-modal sensory input. Multi-layered self-organizing maps are designed and examined in static and dynamic conditions. The results of the tests in the former condition show its capability of robust categorization against noise. The network also shows better performance than a single-layered map does. In the latter condition we focus on shaking behavior by moving only the forearm of the robot. In some combinations of grasping style and shaking radius the network is capable of categorizing two objects robustly. The results show that the network capability to achieve the task largely depends on how to grasp and how to move the objects. These results together with a preliminary simulation are promising toward the self-organization of a high degree of autonomous dynamic object categorization.

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