A method for use by a system including a machine learning, ML model-based video encoder (108, 208); and an ML model-based video decoder (138, 238, 438) comprising a degradation-aware block based Micro-Residual-Network, MicroRN, defined by a number of hidden channels and a number of degradation-aware blocks of the MicroRN, the MicroRN being used as a residual network in a generative adversarial network, GAN, trained decoder, the method comprising: receiving, by the ML model-based video encoder (108, 208), an uncompressed video sequence (116, 216) including a plurality of video frames; determining, by the ML model-based video encoder (108, 208) from among the plurality of video frames, a first video frame subset and a second video frame subset; encoding, by the ML model-based video encoder (108, 208), the first video frame subset to produce a first compressed video frame subset; identifying, by the ML model-based video encoder (108, 208), a first decompression data for the first compressed video frame subset; encoding, by the ML model-based video encoder (108, 208), the second video frame subset to produce a second compressed video frame subset; identifying, by the ML model-based video encoder (108, 208), a second decompression data for the second compressed video frame subset; and transmitting, by the ML model-based video encoder (108, 208) to the ML model-based video decoder (138, 238, 438), the first compressed video frame subset, the second compressed video frame subset, the first decompression data, and the second decompression data; receiving, by the ML model-based video decoder (138, 238, 438), the first compressed video frame subset, the second compressed video frame subset, the first decompression data, and the second decompression data; decoding, by the ML model-based video decoder (138, 238, 438), the first compressed video frame subset using the first decompression data; and decoding, by the ML model-based video decoder (138, 238, 438), the second compressed video frame subset using the second decompression data. receiving, by the ML model-based video encoder (108, 208), an uncompressed video sequence (116, 216) including a plurality of video frames; determining, by the ML model-based video encoder (108, 208) from among the plurality of video frames, a first video frame subset and a second video frame subset; encoding, by the ML model-based video encoder (108, 208), the first video frame subset to produce a first compressed video frame subset; identifying, by the ML model-based video encoder (108, 208), a first decompression data for the first compressed video frame subset; encoding, by the ML model-based video encoder (108, 208), the second video frame subset to produce a second compressed video frame subset; identifying, by the ML model-based video encoder (108, 208), a second decompression data for the second compressed video frame subset; and transmitting, by the ML model-based video encoder (108, 208) to the ML model-based video decoder (138, 238, 438), the first compressed video frame subset, the second compressed video frame subset, the first decompression data, and the second decompression data; receiving, by the ML model-based video decoder (138, 238, 438), the first compressed video frame subset, the second compressed video frame subset, the first decompression data, and the second decompression data; decoding, by the ML model-based video decoder (138, 238, 438), the first compressed video frame subset using the first decompression data; and decoding, by the ML model-based video decoder (138, 238, 438), the second compressed video frame subset using the second decompression data.