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Transmitting high-quality, real-time interactive video over lossy networks is challenging because network data loss can severely degrade video quality. A promising feedback technique for low-latency video repair is Reference Picture Selection (RPS), whereby the encoder selects one of several previous frames as a reference frame for predictive encoding of subsequent frames. RPS can operate in two different modes: an optimistic policy that uses negative acknowledgements (NACKs) and a more conservative policy that relies upon positive acknowledgements (ACKs). The choice between RPS NACK and ACK depends on the network conditions, such as round-trip time and loss probability, and the video content, such as low or high motion. This paper derives a set of analytical models to predict the quality of videos with RPS NACK or ACK. These models are used to study RPS performance under varied network conditions and with different video contents through a series of experiments. Analysis shows that the best choice of ACK or NACK greatly depends upon the round-trip time and packet loss, and somewhat depends upon the video content and Group of Pictures (GOP) size. In particular: 1) RPS ACK performs better than RPS NACK when round-trip times are low; 2) RPS NACK performs better than RPS ACK when the loss rate is low, and RPS ACK performs better than RPS NACK when the loss rate is high; 3) for a given round-trip time, the loss rate where RPS NACK performs worse than RPS ACK is higher for low motion videos than it is for high motion videos; 4) videos with RPS NACK always perform no worse than videos without repair for all GOP sizes; however, 5) below certain GOP sizes, videos without RPS outperform videos with RPS ACK. These insights derived from our models can help determine appropriate choices for RPS NACK and ACK under various network conditions and video contents.