We are witnessing a surge in the popularity and mass-availability of Virtual Reality (VR) technology. Toward enhancing its maturity, current research is focusing on achieving continuous full immersiveness of the VR users during their experiences in Virtual Environments (VEs), as well as on enabling multiuser experiences. In many cases, achieving the full immersiveness requires enabling the users' perception of walking naturally in unbound VEs. Redirected Walking (RDW) algorithms provide a solution to this problem by exploiting the limitations of the human spatial perception. In other words, the RDW algorithms are envisioned to unnoticeably steer the VR users for constraining them within a certain tracking space. Regardless of notable research efforts targeting RDW, there are still not many experimental evaluations of the performance of RDW algorithms, especially in multiuser settings and utilizing contemporary Head-Mounted Devices (HMDs). Toward addressing this issue, we have developed a modular framework that enables straightforward experimentation with single and multiuser RDW in different tracking spaces and for varying VEs. The capabilities of the developed framework were demonstrated by carrying out an extensive experimentation study to capture the performance and noticeability of a contemporary RDW algorithm for a varying number of users in different tracking spaces and VEs. The lessons learned during the study have been conceptualized in a form of a set of RDW design enhancements that can be generically applied to existing RDW algorithms. We show that the proposed enhancements significantly increase the overall performance of RDW and decrease the noticeability of RDW-supported experiences in VEs.