Multicasting has become increasingly important for real-time applications since it is the inherent mode of  delivery in several of them, e.g. teleconferencing, audio and video broadcasting, etc. The Internet, however,  does not provide end-to-end bandwidth or delay guarantees for unicast or for multicast data. As a result, a  number of multiple scale encoding schemes for real-time voice and video transmission have been developed;  these schemes allow adaptation both to the available bandwidth and to the receiver capabilities by varying  the number of levels of the information stream and therefore the corresponding bit rates. Thus, bandwidth  and receiver adaptation of multi-level information streams, in multicast delivery in particular, is an issue  of prime importance for the successful, large-scale deployment of those real-time applications. We propose  to investigate and develop methods for bandwidth sharing and adaptation in the context of multicast-  based real-time applications. A set of fairness criteria based on which the performance of different schemes  can be quantified and compared is introduced. A methodology for finding bandwidth allocations that are  optimal or suboptimal with respect to the different criteria is proposed. Scalability with the network size,  amenability to distributed implementation in terms of required control information and speed of convergence  are adopted as prime criteria, in addition to optimality, in the evaluation of the different algorithms. We  propose an approach for the design of the algorithms that allows the trade-off between optimality with  respect to the performance objective and practicality with respect to the other attributes (i.e. scalability  etc.). A methodology for network control in multicasting in the case of unknown traffic and/or network  conditions is proposed as well. It involves explicit back-pressure based congestion notification feedback and  dynamic per ow scheduling. Finally we propose a method for the design of congestion control policies  with the objective of maximizing an aggregate network utility. A relative comparison between the two  approaches (fairness based and aggregate utility maximization) is also proposed in order to be able to select  the most appropriate one in each case.