  To accommodate the explosive growth of the Internet-of-Things (IoT),
incorporating interference alignment (IA) into existing multiple access (MA)
schemes is under investigation. However, when it is applied in MIMO networks to
improve the system compacity, the incoming problem regarding information delay
arises which does not meet the requirement of low-latency. Therefore, in this
paper, we first propose a new metric, degree of delay (DoD), to quantify the
issue of information delay, and characterize DoD for three typical transmission
schemes, i.e., TDMA, beamforming based TDMA (BD-TDMA), and retrospective
interference alignment (RIA). By analyzing DoD in these schemes, its value
mainly depends on three factors, i.e., delay sensitive factor, size of data
set, and queueing delay slot. The first two reflect the relationship between
quality of service (QoS) and information delay sensitivity, and normalize time
cost for each symbol, respectively. These two factors are independent of the
transmission schemes, and thus we aim to reduce the queueing delay slot to
improve DoD. Herein, three novel joint IA schemes are proposed for MIMO
downlink networks with different number of users. That is, hybrid antenna array
based partial interference elimination and retrospective interference
regeneration scheme (HAA-PIE-RIR), HAA based improved PIE and RIR scheme
(HAA-IPIE-RIR), and HAA based cyclic interference elimination and RIR scheme
(HAA-CIE-RIR). Based on the first scheme, the second scheme extends the
application scenarios from $2$-user to $K$-user while causing heavy
computational burden. The third scheme relieves such computational burden,
though it has certain degree of freedom (DoF) loss due to insufficient
utilization of space resources.
