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In scalable video transmission research, the video transmission process is commonly modeled as a Markov decision process, where deep reinforcement learning (DRL) methods are employed to optimize the wireless transmission of scalable videos. Furthermore, the adaptive DRL algorithm can address the energy shortage problem caused by the uncertainty of energy capture and accumulated storage, thereby reducing video interruptions and enhancing user experience. To further optimize resources in wireless energy transmission and tackle the challenge of balancing exploration and exploitation in the DRL algorithm, this paper develops an adaptive DRL algorithm that extends classical DRL frameworks by integrating dropout techniques during both the training and prediction processes. Moreover, to address the issue of continuous negative rewards, which are often attributed to incomplete training in the wireless video transmission DRL algorithm, this paper introduces the Cramér large deviation principle for specific discrimination. It identifies the optimal negative reward frequency boundary and minimizes the probability of misjudgment regarding continuous negative rewards. Finally, experimental validation is performed using the 2048-game environment that simulates wireless scalable video transmission conditions. The results demonstrate that the adaptive DRL algorithm described in this paper achieves superior convergence speed and higher cumulative rewards compared to the classical DRL approaches.

Efficient video transmission over low-bandwidth and unstable networks remains a central challenge for real-time applications such as telemedicine, remote surveillance, and edge-based video analytics. Conventional adaptive streaming approaches such as DASH and HLS operate primarily at the application layer, adjusting bitrates reactively based on buffer occupancy or short-term throughput. These strategies often fail under abrupt bandwidth fluctuations, leading to quality oscillations and excessive rebuffering. This paper proposes a cross-layer bitrate optimization framework that unifies lightweight adaptive encoding with a control-theoretic feedback loop driven by real-time network metrics. The framework jointly considers content complexity, encoder parameters, and network congestion signals to dynamically regulate bitrate across both the network and application layers. A lightweight encoder enhancement module performs perceptually guided bit allocation using saliency-aware analysis, while the control loop ensures fast convergence of target bitrate and stability against throughput variability. Extensive experiments across Wi-Fi, 4G, and simulated edge-network traces show that the proposed system achieves 30–40% bitrate reduction compared with H.264/H.265 adaptive streaming baselines, with PSNR gains up to 1.2 dB and SSIM improvements of 0.02, while reducing buffering time by over 35%. These results establish that the synergy of control-theoretic adaptation and lightweight encoding yields a scalable, low-complexity solution suitable for next-generation low-bitrate video communication systems operating on mobile and edge devices.

Optical wireless video transmission technology combines the advantages of high data rates, enhanced security, large bandwidth capacity, and strong anti-interference capabilities inherent in optical communication, establishing it as a pivotal technology in contemporary data transmission networks. However, video data comprises a large volume of image information, resulting in substantial data flow with significant redundant bits. To address this, we propose an adaptive block sampling compressive sensing algorithm that overcomes the limitations of sampling inflexibility in traditional compressive sensing, which often leads to either redundant or insufficient local sampling. This method significantly reduces the presence of redundant bits in video images. First, the sampling mechanism of the block-based compressive sensing algorithm was optimized. Subsequently, a wireless optical video transmission experimental system was developed using a Field-Programmable Gate Array chip. Finally, experiments were conducted to evaluate the transmission of video optical signals. The results demonstrate that the proposed algorithm improves the peak signal-to-noise ratio by over 3 dB compared to other algorithms, with an enhancement exceeding 1.5 dB even in field tests, thereby significantly optimizing video transmission quality. This research contributes essential technical insights for the enhancement of wireless optical video transmission performance.

The millimeter-wave wireless transmission system is widely regarded as a promising solution for applications of future 6G communication. This paper presents an experimental comparison between all-optical and all-electric receivers for millimeter-wave communication systems over a 15 m wireless link and demonstrates 200 m and 2 km real-time uncompressed HD video transmission using an all-optical transceiver at 100 GHz. The systems leverage photonics-assisted heterodyne beating techniques at the transmitter, while the receivers employ either an avalanche photodiode (APD)-based all-optical approach or an envelope detection-based all-electric approach. Experimental results show that the all-optical transceiver supports significantly higher transmission rates, achieving error-free transmission at up to 11.318 Gbps over a 200 m wireless link without clock recovery, compared to the all-electric receiver, which is limited to only 3.125 Gbps error-free 15 m transmission. This work proves that the proposed system based on the all-optical receiver is more promising for supporting future 6G scenarios requiring ultra-wideband, high capacity, and wide coverage high-speed wireless communications.

At present, Internet of Things (IoT) networks are attracting much attention since they provide emerging opportunities and applications. In IoT networks, the asymmetric and symmetric studies on medical and biomedical video transmissions have become an interesting topic in both academic and industrial communities. Especially, the transmission process shows the characteristics of asymmetry: the symmetric video-encoding and -decoding processes become asymmetric (affected by modulation and demodulation) once a transmission error occurs. In such an asymmetric condition, the quality of service (QoS) of such video transmissions is impacted by many different factors across the physical (PHY-), medium access control (MAC-), and application (APP-) layers. To address this, we propose a cross-layer optimization-based strategy for asymmetric medical video transmission in IoT systems. The proposed strategy jointly utilizes the video-coding structure in the APP- layer, the power control and channel allocation in the MAC- layer, and the modulation and coding schemes in the PHY- layer. To obtain the optimum configuration efficiently, the proposed strategy is formulated and proofed by a quasi-convex problem. Consequently, the proposed strategy could not only outperform the classical algorithms in terms of resource utilization but also improve the video quality under the resource-limited network efficiently.

Packet loss of video streams cannot be avoided at wireless links for limited wireless bandwidth and frequently changed environments. To provide differentiated Quality of Service (QoS) guarantees between multimedia and data services, IEEE 802.11e was proposed. However, its performance and flexibility need to be further improved. In this paper, after a survey on various modifications of IEEE 802.11e, we formulate the problem of video transmission over IEEE 802.11e networks to help scheme design and performance analysis. Then accompanied with in-depth analysis, an adaptive unequal protection schema is proposed, which is composed of three mechanisms: (1) Insert each video packet into the access category (AC) with the minimum relative queuing delay; (2) Assign each packet dynamically to a proper AC based on several parameters to guarantee the transmission of high priority frames; (3) Apply fuzzy logic controllers to adjust parameters so as to reply quickly to the variation of video data rate, coding structure and network load. Finally, regarding MPEG-4 codec as the example, we perform extensive evaluations and validate the effectiveness and flexibility of proposed scheme. Simulations are divided into WLAN and multihop parts, involving different video sequences and various traffic modes of data streams. Beside performance comparison between proposed scheme and other ones, influence of parameter setting and combination with routing algorithms are also evaluated.

The cumulative effects of network transmission link imperfections and real-time limitations of video data result in multiple challenges for mobile video transmission. The challenges, together with the increasing expectations of users for well-displayed videos, further extend the complexity particularly in dense areas. To address the issues and provide efficient mobile video transmission, this work proposes a model with two phases: network-related settings (NRS) and video-related settings (VRS). The NRS is related to the mobile transmission link limits and develops four separate mobile networks, including long term evolution, 802.11 ax dual-band, and 802.11 ac. The VRS, on the other hand, is related to the video real-time constraints and covers five well-known compression algorithms used in the reference video preparation process. With comprehensibility in mind, the model comprises different factors that have a profound impact on the mobile video transmission process. The model is implemented and the results determine the video delivery efficiency in terms of the network-level quality of service and service-level quality of experience. To further validate the model, a testbed is set up and the measured experimental results are compared with those of the simulation. The results establish a baseline for efficient mobile video transmission on resource constraint devices.

Recently, there have been significant advancements in the study of semantic communication in single-modal scenarios. However, the ability to process information in multi-modal environments remains limited. Inspired by the research and applications of natural language processing across different modalities, our goal is to accurately extract frame-level semantic information from videos and ultimately transmit high-quality videos. Specifically, we propose a deep learning-based Multi-Modal Mutual Enhancement Video Semantic Communication system, called M3E-VSC. Built upon a Vector Quantized Generative Adversarial Network (VQGAN), our system aims to leverage mutual enhancement among different modalities by using text as the main carrier of transmission. With it, the semantic information can be extracted from key-frame images and audio of the video and perform differential value to ensure that the extracted text conveys accurate semantic information with fewer bits, thus improving the capacity of the system. Furthermore, a multi-frame semantic detection module is designed to facilitate semantic transitions during video generation. Simulation results demonstrate that our proposed model maintains high robustness in complex noise environments, particularly in low signal-to-noise ratio conditions, significantly improving the accuracy and speed of semantic transmission in video communication by approximately 50 percent.

In wireless networks, multi-user video streaming under limited resource is a challenging problem. The main challenge is how to meet the transmission requirements under the different channel condition and video content complexity. In this paper, we propose an uncoded video transmission framework to deliver the multi-user video over wireless networks. In order to evaluate the overall performance of multi-user network more practically, three optimization strategies are proposed in this paper: 1) minimizing the total distortion; 2) minimizing the maximal distortion; 3) minimizing the summation of square root distortion. Furthermore, the corresponding joint resource allocation algorithms are developed to solve the optimization problems. The simulation results demonstrate that different optimization strategies have different resource allocation and performance for each user. The optimization strategy 1) performs the best in terms of average PSNR of all users, the optimization strategy 2) achieves more fair result, and the optimization strategy 3) achieves a good balance. The strategy 3) can improve the performance for users with bad channel condition, while little loss is caused for users with good channel condition.

Video signals are a very important part of multimedia applications. Due to limited network bandwidth, video signals are subjected to the compression process, which introduces different compression artifacts. During network transmission, additional artifacts are introduced in video signals due to random bit errors and packet loss (PL). Both mentioned artifact types degrade visual quality of the video signal and thus, it has to be continuously monitored to ensure the required quality of service (QoS) provided to end users. An important component of the video quality monitoring system deals with video transmission artifact detection. In this paper, a no-reference (NR) pixel-based video transmission artifact detection algorithm is proposed, called the packet loss area measure (PLAM) algorithm. When detecting video transmission artifacts, the PLAM algorithm takes into account spatial and temporal information of a video signal. The performance of the proposed PLAM algorithm has been compared to those of the three existing different PL detection algorithms on a broad set of significantly different video signals from two publicly available video databases. One of these databases, called the Referent Packet Loss (RPL) database, has been created within this research and is presented in this paper. The algorithm performance testing results show that PLAM achieves high performance and overcomes other tested algorithms. Furthermore, the results show that the PLAM algorithm is very robust when detecting video transmission artifacts in video signals of different contents, with distinct degradation levels and PL error-concealment methods used in decoder post-processing. Due to its low computational complexity, the PLAM algorithm is capable of processing Full HD and Ultra HD video signals with the frame rate up to 100 and 25 frames per second (fps), respectively, in real time, in the case when high-end CPU is used.