The proliferation of small cells (SCs) is ubiquitous in boosting system capacity, coverage, and quality of services (QoS) for smart applications. However, due to the dynamic users and constrained resources, SCs cannot afford to have a large quantization codebook, which is mostly suitable for the macro cells (MCs) with finite rate feedback (FRF)-based multiple input single output (MISO) systems. This paper proposes a per-receiver antenna-based adaptive quantization approach for SCs where the codebook size changes w.r.t. the number of receiver antennas. We further address the code quantization error (CQE), which could occur due to the quantization of two unique channels with the same code and average system error (AvgSysErr), which could increase due to the rise in the CQE. We show that the convergence of AvgSysErr with FRF-based MISO systems requires SCs to have the non-unique code probability of the quantization codebook less than 1 N , where N is the number of antennas at the transmitter. Similarly, the lower-bound for the non-unique code probability needs to be less than or equal to ε, where ε is the difference between the non-unique code probability of 1 N 1 and 1 N 2 , given 1 N 1 > 1 N 2 (where N1 and N2 are the number of antennas at the transmitter). Additionally, interference from the adjacent SCs is inevitable due to their dense deployments satisfying the low signal-to-interference-plus-noise ratio (SINR) regime. We quantify the minimum interference constraint (IC) for SCs, which is critical for the multiplexing gain in the low SINR regime. We use the iterative steepest ascent (SA) method and quantify ICs for SCs. With the proof of convergence and the comparative analysis of the ideal and real cases, supported with our simulated results, we show that the expected value of IC is the best IC to be considered for SCs.