Feature-based Visual Simultaneous Localization and Mapping (V-SLAM) employs feature descriptors to recognize landmarks in successive frames. For networked (offloaded and/or collaborative) V-SLAM, the feature descriptors typically need to be transmitted over wireless communication networks with limited bitrates. Conventionally, V-SLAM systems have employed general computer vision feature descriptors whose size (in bytes) accounts for most of the data transmission. We develop a probabilistic model of the V-SLAM feature matching and identify three parameters that govern the probability of the correct feature match: the feature set size, the bit error probability between feature descriptors observing the same landmark, and the probability that the correct feature match exists in the feature set. Based on these three parameters, we formulate a research agenda for achieving high V-SLAM performance (i.e., high correct feature match probability) for reduced V-SLAM feature descriptor sizes (and thus reduced network throughput requirements). We address the first item on this research agenda by pursuing the reduction of the feature set size through a novel Orientation Restriction with Frame-to-Frame Projection (ORFFP). We evaluate ORFFP in comparison to the state-of-the-art ORB-SLAM2 approach using three representative V-SLAM test sequences. Our ORFFP approach reduces the size of the feature set to as little as 9.1% of ORB-SLAM2, allowing for the use of smaller feature descriptors. Thereby, ORFFP reduces the overall required throughput for networked V-SLAM down to roughly a third of the current approach, while achieving essentially the same V-SLAM performance. We also find that for learned descriptor-based models (HashSIFT, BAD), the feature descriptor size can be reduced using ORFFP without severely degrading the V-SLAM performance. We outline future research directions towards comprehensively addressing the formulated research agenda for enabling networked V-SLAM on low-bitrate networks.