We present a low-overhead media streaming system, called SRMS (Scalable Resilient Media Streaming) that can be used to scalably deliver streaming data to a large group of receivers. SRMS uses overlay multicast for data distribution to a large group of users. SRMS leverages a probabilistic
loss recovery technique to provide high data delivery guarantees even under large network losses and overlay node failures. Through detailed analysis in this paper, we show that this loss recovery technique (and
consequently SRMS) has efficient scaling properties --- the overheads at each overlay node asymptotically decrease to zero with increasing group sizes.

We also present a detailed description of the SRMS architecture. The clients in the SRMS system are able to interoperate with existing media streaming servers that use RTP for data transport. One of the interesting features of SRMS is that it can simultaneously support clients with
disparate access bandwidths. It enables the necessary bandwidth adaptations using standard Real-time Transport Protocol (RTP) mechanisms, e.g. RTP translators.

We have implemented and evaluated the SRMS system in detail on an emulated network as well as on a wide-area testbed with up to 128 clients. Our results show that clients using SRMS achieve high (> 97%) data delivery ratios with low overheads (< 5%) even for very high failure rates (upto five per minute). (UMIACS-TR-2003-51)