Adaptive streaming based on DASH offers personalized video experience and smooth playback by allowing dynamical adjustments of the video bitrate to the variations of network conditions. This is especially important for current and future Internet video streaming applications, including emerging ones such as virtual reality-based, as adaptive streaming plays a key role in providing high quality viewing experience, especially in limited bandwidth delivery environments. To enable this promising avenue in a 5G context, efforts are made to consider it alongside multicast and edge caching, as part of the next generation communication technology. In this paper, we model the adaptive streaming transmission problem in a mobile scenario as a multi-source multicast multi-rate problem (MMMP) whose linear relaxation is concave. We decompose the problem in terms of clients and propose the distributed delivery algorithm (DDA). The computation complexity, convergence and time-varying adaptation of DDA are theoretically analyzed. Additionally, to further reduce the computation complexity of the solution, a heuristic approximation method (H-DDA) based on the physical meaning of the problem is proposed and it is also shown how H-DDA converges to the optimal value by numerical means. Finally, we conduct a series of simulation tests to demonstrate the superiority of the proposed HDDA in comparison with other state-of-art solutions.

The development of Metaverse industry produces many 360 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{\circ}$</tex-math> </inline-formula> images and videos. Transmitting these images or videos efficiently is the key to success of Metaverse. Since the subject’s field of view is limited in Metaverse, from the perception perspective, bit rates can be saved by focusing video encoding on salient regions. On different ways of handling 360 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{\circ}$</tex-math> </inline-formula> image projections, the existing works either consider combining local and global projections or just use only global projection for saliency prediction, which results in slow detection speed or low accuracy. In this work, we address this problem by Embedding a self-supervised Proxy task in the Saliency prediction Network, dubbed as <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EPSNet</b> . The main architecture follows an autoencoder with an encoder for feature extraction and a decoder for saliency prediction. The proxy task is combined with the encoder to enforce it to learn local and global information. It is designed to find the location of a certain local projection in the global projection via self-supervised learning. A cross-attention fusion mechanism is used to fuse the global and local features for location prediction. Then, the decoder is trained based on the sole global projection. In this way, the time-consuming local-global feature fusion is placed in the training stage only. Experiments on public dataset show that our method has achieved satisfactory results in terms of inference speed and accuracy. The dataset and code are available at https://github.com/zzz0326/EPSNet.

In this paper, a framework based on two feature extraction networks and a multilevel feature fusion (MFF) network is proposed. Multilevel degradation features can be obtained through this method, and combined with the human visual perception system, the local and global feature information contained in these features can be captured, which is conducive to the prediction of distorted images. First, a restored image approximating a reference image is generated by a restorative generative adversarial network (GAN). Furthermore, the multilevel degradation features of distorted images and the restored image features are extracted by EfficientNet. Second, the features extracted by EfficientNet are input into the MFF network and are fully expressed by the top-down, bottom-up and third edge joining methods. Moreover, the features provide more low-level details and high-level semantic features for the prediction of image quality scores. In addition, after the MFF stage, the framework calculates the score of each branch feature and obtains the average quality score. Experimental results show that our method achieves greatly improved prediction accuracy and performance on five standard databases.

With the ever-increasing demand for data services, the spectrum becomes precious resource. The cooperative transmission mechanism, which allows broadcast and unicast to share the same spectrum, can effectively alleviate this situation. The success of LDM in ATSC 3.0 has inspired many scholars to superimpose broadcast and unicast services with a fixed power injection ratio. To further improve radio resource utilization, A flexible LDM scheme with a variable power injection ratio is proposed. The scheduling policy in Medium Access Control layer is formulated as a sequential decision problem, and it proved to have Markov property. A DRL-based algorithm is proposed to obtain the optimal scheduling scheme. Considering the requirements of broadcasting service and unicast services, the Lyapunov optimization method is used to convert the problem to the Lyapunov penalty form that is more conducive for the agent to learn the optimal strategy. Simulation results show that the proposed scheme can achieve much improved performance in system throughput when guaranteeing the quality of the broadcast service.

An accurate no-reference image quality assessment metric for compression artifacts is essential for the broadcasting and streaming industries. Although we have witnessed impressive advances in the capturing, delivery and display technologies, we have not managed to match them with an accurate and perceptual based no-reference image quality metric. In this paper, we propose a unique perceptual based no-reference quality metric for compressed HD frames/images that is based on the DenseNet network architecture. We focus on the effect HEVC (High Efficiency Video Coding) compression artifacts have on the visual quality of a broadcasted and streamed video, as this is a requirement of immense importance for these industries. We chose the Video Multi-Method Assessment Fusion (VMAF) metric as our base measure to map visual quality of HEVC compression artifacts to five visual quality levels. The original VMAF classification was changed to reflect High Definition (HD) resolution images. We trained a DenseNet network to classify compressed images into five visual categories using the dataset generated by the modified VMAF. DenseNet was chosen for its ability to process HD images. Our evaluations have shown that our no-reference metric achieves an impressive average accuracy of 94.13% in classifying the visual quality of compressed images.

In the near future, the broadcasting scenario will be characterized by immersive content. One of the systems for capturing the 3D content of a scene is the Light Field imaging. The huge amount of data and the specific transmission scenario impose strong constraints on services and applications. Among others, the evaluation of the quality of the received media cannot rely on the original signal but should be based only on the received data. In this direction, we propose a no-reference quality metric for light field images which is based on spatial and angular characteristics. In more details, the estimated saliency and cyclopean maps of light field images are exploited to extract the spatial features. The angular consistency features are, instead, measured with the use of the Global Luminance Distribution knowledge and the Weighted Local Binary Patterns operator on Epipolar Plane Images. The effectiveness of the proposed metric is assessed by comparing its performance with state-of-the-art quality metrics using 4 datasets: SMART, Win5-LID, VALID 10-bit, and VALID 8-bit. Furthermore, the performance is analyzed in cross-datasets, with different distortions, and for different saliency maps. The achieved results show that the performance of the proposed model outperforms state-of-the-art approaches and perform well for different distortion types and with various saliency models.

The dependent quantization (DQ) technique in Versatile Video Coding (VVC) provides high compression efficiency, but it suffers from much computation, which is not preferred in practical applications. In this context, we propose a complexity-efficient dependent quantization structure employing a machine learning-based DQ start position prediction and a simplified trellis structure. Firstly, the proposed DQ start position is decided by a binary classifier which finds an appropriate location to start the DQ process. To further reduce the processing time of the binary classification, we also set up a precondition with a learned preset threshold which can reduce the number of coefficients subjected to the classifier. Based on our statistical analysis showing that under certain conditions some branches of the trellis structure are not necessary to check, we also design a branch pruning method and a removal scheme of all-zero coefficient group checking to simplify the DQ trellis structure. Our proposed complexity-efficient DQ has much fewer calculations of rate-distortion costs. Our experimental results show that it can reduce the computational complexity of the DQ process with nearly no performance loss of Bjontegaard delta bit rate (BDBR) irrespective of the all intra or the random access configurations.

This article analyses low-density parity-check (LDPC) codes for forward error correction (FEC) in the next generation of digital terrestrial broadcasting systems for transmitting high-volume content services, such as ultra high definition television (UHDTV). We assess the performance of the LDPC codes used by the Advanced ISDB-T system with 2x2 MIMO transmission using experimental data obtained in field measurements in Brazil. Channel power, carrier-to-noise threshold, and receiving threshold were measured for four different low-rates LDPC codes, two modulation types, and three receiving antenna heights at outdoor sites in urban areas. The coverage analysis shows that the channel power presents a similar distribution for horizontal and vertical polarized signals. C/N threshold analysis shows that in sites with high vehicular traffic, the C/N threshold and reception threshold values were much higher than in other sites with less heavy vehicular traffic. Among the five coding-modulation combinations used in the field tests, the transmission configuration with LDPC code rate 2/16 and QPSK modulation was the one that presented the best results partially meeting the requirement of C/N <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\leq $</tex-math> </inline-formula> 0dB. The transmission configuration with LDPC code rate 3/16 and QPSK modulation showed C/N results just above 0dB, but offering 50% higher data transmission rate.