To support ultra-reliable low-latency communications (URLLC) for time-sensitive multimedia 5G wireless services, several advanced techniques, including statistical delay-bounded quality-of-service (QoS) provisioning and finite blocklength coding (FBC), have been developed to upper-bound both delay and error- rate. On the other hand, millimeter wave (mmWave) cell-free (CF) massive multi-input multi-output (m-MIMO) techniques, where a large number of distributed access points (APs) jointly serve all users at millimeter wave frequencies using the same time- frequency resources, has emerged as one of the key promising candidate techniques to significantly improve QoS performance in 5G networks. Leveraging the sparse scattering characteristics of mmWave wireless channels, the arrival traffic can be partitioned into parallel substreams using scattering-clusters based mmWave wireless channel model to reduce queuing delay. However, due to the complexity of analyzing queueing dynamics across clustered mmWave wireless channels for CF m-MIMO schemes, it is challenging to statistically guarantee QoS performance in terms of upper-bounding delay and error-rate. To overcome the above- mentioned problems, in this paper we propose a novel analytical model to quantitatively characterize stochastic QoS performance of delay and error-rate across clustered mmWave channels for CF m-MIMO schemes. In particular, we develop CF m-MIMO system models across clustered mmWave wireless channels. We also apply the Mellin transform to derive an upper bound on the delay violation probability using the spatial multiplexing queue model. Our simulation results validate and evaluate our proposed FBC based mmWave CF m-MIMO schemes under statistical delay/error-rate bounded QoS constraints.