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Many congestion control protocols use explicit feedback from the network to achieve high performance. Most of these either require more bits for feedback than are available in the IP header or incur performance limitations due to inaccurate congestion feedback. There has been recent interest in protocols that obtain high-resolution estimates of congestion by combining the explicit congestion notification (ECN) marks of multiple packets, and using this to guide multiplicative increase, additive increase, multiplicative decrease (MI-AI-MD) window adaptation. This paper studies the potential of such approaches, both analytically and by simulation. The evaluation focuses on a new protocol called Binary Marking Congestion Control (BMCC). It is shown that these schemes can quickly acquire unused capacity, quickly approach a fair rate distribution, and have relatively smooth sending rates, even on high bandwidth-delay product networks. This is achieved while maintaining low average queue length and negligible packet loss. Using extensive simulations, we show that BMCC outperforms XCP, VCP, MLCP, CUBIC, CTCP, SACK, and in some cases RCP, in terms of average flow completion times. Suggestions are also given for the incremental deployment of BMCC.