With the increasing demand for internet of things (IoT) applications, machine-type video communications have become an indispensable means of communication. It is changing the way we live and work. In machine-type video communications, the quality and delay of the video transmission should be guaranteed to satisfy the requirements of communication devices at the condition of limited resources. It is necessary to reduce the burden of transmitting video by losing frames at the video sender and then to increase the frame rate of transmitting video at the receiver. In this paper, based on the pretrained network, we proposed a frame rate up-conversion (FRUC) algorithm to guarantee low-latency video transmitting in machine-type video communications. At the IoT node, by periodically discarding the video frames, the video sequences are significantly compressed. At the IoT cloud, a pretrained network is used to extract the feature layers of the transmitted video frames, which is fused into the bidirectional matching to produce the motion vectors (MVs) of the losing frames, and according to the output MVs, the motion-compensated interpolation is implemented to recover the original frame rate of the video sequence. Experimental results show that the proposed FRUC algorithm effectively improve both objective and subjective qualities of the transmitted video sequences.

Text classification plays an important role in various applications of big data by automatically classifying massive text documents. However, high dimensionality and sparsity of text features have presented a challenge to efficient classification. In this paper, we propose a compressive sensing- (CS-) based model to speed up text classification. Using CS to reduce the size of feature space, our model has a low time and space complexity while training a text classifier, and the restricted isometry property (RIP) of CS ensures that pairwise distances between text features can be well preserved in the process of dimensionality reduction. In particular, by structural random matrices (SRMs), CS is free from computation and memory limitations in the construction of random projections. Experimental results demonstrate that CS effectively accelerates the text classification while hardly causing any accuracy loss.

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In 6G network, lots of edge devices facilitates the low-latency transmission of video. However, with limited processing and storage capabilities, the edge devices cannot afford to reconstruct the vast amount of video data. On the condition of edge computing in 6G network, this paper fuse a self-similarity based context feature into Frame Rate Up-Conversion (FRUC) to generate the pseudo-true video sequences at high frame rate, and its core is the extraction of context layer for each video frame. First, we extract the patch centered at each pixel, and use the self-similarity descriptor to generate the correlation surface. Then, the expectation or skewness of correlation surface in statistics is computed to represent its context feature. By attaching an expectation or a skewness to each pixel, the context layer is constructed, and added to the video frame as a new channel. According to the context layer, we predict the motion vector field of absent frame by using the bidirectional context match, and finally produce the interpolated frame. From the experimental results, it can be seen that by deploying the proposed FRUC algorithm on edge devices, the output pseudo-true video sequences have the satisfying objective and subjective qualities.