Efficient delivery of video data over computer networks has been studied extensively for decades. Still, multi-receiver video delivery is challenging, due to heterogeneity and variability in network availability, end node capabilities, and receiver preferences. Our earlier work has shown that content-based networking is a viable technology for fine granularity multi-receiver video streaming. By exploiting this technology, we have demonstrated that each video receiver is provided with fine grained and independent selectivity along the different video quality dimensions region of interest, signal to noise ratio for the luminance and the chrominance planes, and temporal resolution. Here we propose a novel adaptation scheme combining such video streaming with state-of-the-art techniques from the field of adaptation to provide receiver-driven multi-dimensional adaptive video streaming. The scheme allows each client to individually adapt the quality of the received video according to its currently available resources and own preferences. The proposed adaptation scheme is validated experimentally. The results demonstrate adaptation to variations in available bandwidth and CPU resources roughly over two orders of magnitude and that fine grained adaptation is feasible given radically different user preferences.