In this paper we introduce TFMCC, an equation-based multicast congestion control mechanism that extends the TCP-friendly TFRC protocol from the unicast to the multicast domain. The key challenges in the design of TFMCC lie in scalable round-trip time measurements, appropriate feedback suppression, and in ensuring that feedback delays in the control loop do not adversely affect fairness towards competing flows. A major contribution is the feedback mechanism, the key component of end-to-end multicast congestion control schemes. We improve upon the well-known approach of using exponentially weighted random timers by biasing feedback in favor of low-rate receivers while still preventing a response implosion. We evaluate the design using simulation, and demonstrate that TFMCC is both TCP-friendly and scales well to multicast groups with thousands of receivers. We also investigate TFMCC's weaknesses and scaling limits to provide guidance as to application domains for which it is well suited. Keywords congestion control, multicast, single-rate, TCP-friendliness, feedback suppression 1.

The Real-time Transport Control Protocol (RTCP) is a crucial mechanism used, amongst other things, for synchronisation and feedback control in multimedia sessions. However as groups grow to large numbers, it faces two serious challenges: the growing deployment of unidirectional and asymmetric broadcast architectures, such as Source-Specific Multicast and satellite networks, eliminate the shared control backchannel on which RTCP relies; the per-receiver RTCP reporting frequency diminishes prohibitively due to the bandwidth-sharing algorithm. We present new algorithmic techniques that enable RTCP to combat these issues, allowing it to function in a wider range of environments and to scale to larger groups.

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Abstract—Physical phenomena such as temperature, humidity, and wind velocity often exhibit both spatial and temporal correlation. We consider the problem of tracking the extremum value of a spatio-temporally correlated field using a wireless sensor network. Determining the extremum at the fusion center after making all sensor nodes transmitting their measurements is not energy-efficient because the spatio-temporal correlation of the field is not exploited. We present an optimal centralized algorithm that utilizes the aforementioned correlation to not only minimize the number of transmitting sensors but also ensure low tracking error with respect to the actual extremum. We use recent order statistics bounds in the formulation of the cost function. Since the centralized algorithm has high time complexity, we propose a suboptimal distributed algorithm based on a modified cost function. Our simulations indicate that a small fraction of sensors is often sufficient to track the extremum, and that the centralized algorithm can achieve about 70 % energy savings with almost perfect tracking. Furthermore, the performance of the distributed algorithm is comparable to that of the centralized algorithm with up to 25 % more energy expenditure. I.