MS-VBRVQ: Multi-scale variable bitrate speech residual vector quantization

With the widespread adoption of consumer electronic devices such as virtual reality (VR) headsets, panoramic cameras, and ultra-high-definition displays, omnidirectional (360°) images have become increasingly important for providing immersive user experiences. However, the high resolution and data volume of these images pose significant challenges for bandwidth-limited and resource-constrained consumer electronics. To address these challenges, based on an advanced parallel dual-branch hybrid architecture (TCM) consisting of convolutional neural networks (CNNs) and Swin Transformer, we propose a dual-prompt learned variable bitrate omnidirectional image compression framework, termed DPVOC, which utilizes distortion maps (Dmaps) and quality maps (Qmaps) as dual prompts to enable region-adaptive bit allocation and achieve efficient variable bitrate compression. Specifically, during training, to alleviate the computational burden of processing entire ERP images, we randomly crop ERP images into patches as input to the network. Considering the varying degrees of distortion redundancy across different regions of ERP patches, we introduce corresponding Dmap patches to record the local distortion levels. In the CNN branch, the patch-wise uniform Qmaps are element-wise multiplied with the Dmaps to modulate the CNN features. In the Swin Transformer branch, the uniform Qmap patches are used as prompts in the attention mechanism to guide the feature embeddings for adaptability to bitrate variations. Additionally, Dmap patches are introduced into the feedforward network (FFN) of the Swin Transformer to suppress redundant information. By incorporating fine-grained and symmetric prompts from both Qmaps and Dmaps into the encoder and decoder through the dual-branch structure, our networks can effectively adapt to diverse bitrate requirements. During inference, entire Qmaps and Dmaps are used as inputs, and their bitrate overhead is negligible. Experimental results demonstrate that DPVOC achieves superior performance in omnidirectional image compression while maintaining low computational complexity.

Recently, foundational diffusion models have attracted considerable attention in image compression tasks, whereas their application to video compression remains largely unexplored. In this article, we introduce DiffVC, a diffusion-based perceptual neural video compression framework that effectively integrates foundational diffusion model with the video conditional coding paradigm. This framework uses temporal context from previously decoded frame and the reconstructed latent representation of the current frame to guide the diffusion model in generating high-quality results. To accelerate the iterative inference process of diffusion model, we propose the Temporal Diffusion Information Reuse (TDIR) strategy, which significantly enhances inference efficiency with minimal performance loss by reusing the diffusion information from previous frames. Additionally, to address the challenges posed by distortion differences across various bitrates, we propose the Quantization Parameter-based Prompting (QPP) mechanism, which utilizes quantization parameters as prompts fed into the foundational diffusion model to explicitly modulate intermediate features, thereby enabling a robust variable bitrate diffusion-based neural compression framework. Experimental results demonstrate that our proposed solution delivers excellent performance in both perception metrics and visual quality.

Background: Higher power consumption raises chip temperature because it draws more current from the power source, which directly affects how long the batteries survive in portable devices. High temperature affects the dependability and functionality of a circuit, requiring more complex packaging and cooling strategies. One of the most significant challenges in VLSI design is power consumption. The power consumption of the circuit rises with both transistor density and chip complexity. In addition, one of the essential building blocks of hardware in the majority of VLSI applications and digital signal processing systems is the multiplier. Aims and objective: This study aimed to design and compare array multiplier, Vedic multiplier, and Wallace tree multiplier using variable bit lengths. Methodology: In this paper, authors designed array multiplier, Vedic multiplier, and Wallace tree multiplier using variable bit lengths. For comparison, the VIVADO tool was used to simulate and synthesize multiplier outputs. Results: Wallace tree multipliers resulted in 31.153mW, 13.220mW, 4.099mW, and 0.988 mW of power dissipation for 16-bit, 8-bit, 4-bit, and 2-bit, respectively. The best multiplier was designed using different logic like AOI, OAI, NAND-NAND, and NOR-NOR and was compared based on power dissipation. It was observed that 2.256mW power dissipation was observed for NOR-NOR logic, which was minimal among other logics. Conclusion: The 4-bit Wallace multiplier using NOR-NOR logic was used for FPGA implementation, which can be used in digital signal processing applications.

With the emergence of next-generation video applications and increasing spatial resolutions, delivering high-quality video is still limited by network bandwidth. Adaptive bitrate (ABR) can select the appropriate bitrate for video streaming based on bandwidth, which can mitigate rebuffering caused by insufficient bandwidth. In comparison to Constant Bitrate (CBR), Variable Bitrate’s (VBR) encoding scheme can achieve the same quality with less bandwidth consumption and is gradually being widely used in ABR streaming. However, the quality of the video is still degraded due to a poor network. Recent research utilizes Super-resolution (SR) in ABR streaming to construct neural Video Quality Enhancement (VQE) systems, thereby improving the quality of video segments downloaded due to insufficient bandwidth. However, SR cannot participate in the downsampling encoding process of videos, which results in the effectiveness of existing SR-based VQE systems being inherently limited due to unavoidable information loss during downsampling encoding. Concurrently, SR’s high computational cost restricts neural VQE systems’ deployment on clients without GPUs. In contrast to the unidirectional workflow of SR, Rescaling can be integrated into the downsampling encoding process of videos, allowing favorable information to be retained for VQE. To implement high-quality real-time VQE for ABR streaming of VBR-encoded videos on CPUs, we propose RePC, a novel neural VQE system framework for optimizing existing neural VQE systems based on Rescaling (Re) for the first time, and Patch Content-awareness (PC). In detail, RePC uses Rescaling instead of SR to achieve better VQE by participating in the video downsampling. We also propose a Video Single-Image Rescaling model, VSIR, to indicate the effectiveness of RePC in quality enhancement. To speed up VQE, RePC designs a PC algorithm to mix interpolation and neural computation based on the practical upsampling ability. Our evaluation results demonstrate quality gains of 0.55–2.96 dB in PSNR and 1.79–3.18 in VMAF with fewer parameters, a speed-up of 15×–286× well up to real-time requirements on CPUs, and Quality of Experience (QoE) improvements of 16.58–26.65 are also achieved in an ABR system under various networking conditions.

Neural Radiance Field (NeRF)-based volumetric video has revolutionized visual media by delivering photorealistic Free-Viewpoint Video (FVV) experiences that provide audiences with unprecedented immersion and interactivity. However, the substantial data volumes pose significant challenges for storage and transmission. Existing solutions typically optimize NeRF representation and compression independently or focus on a single fixed rate-distortion (RD) tradeoff. In this paper, we propose VRVVC, a novel end-to-end joint optimization variable-rate framework for volumetric video compression that achieves variable bitrates using a single model while maintaining superior RD performance. Specifically, VRVVC introduces a compact tri-plane implicit residual representation for inter-frame modeling of long-duration dynamic scenes, effectively reducing temporal redundancy. We further propose a variable-rate residual representation compression scheme that leverages a learnable quantization and a tiny MLP-based entropy model. This approach enables variable bitrates through the utilization of predefined Lagrange multipliers to manage the quantization error of all latent representations. Finally, we present an end-to-end progressive training strategy combined with a multi-rate-distortion loss function to optimize the entire framework. Extensive experiments demonstrate that VRVVC achieves a wide range of variable bitrates within a single model and surpasses the RD performance of existing methods across various datasets.

The imperative for compression of material textures emerges from the critical demand for high-quality rendering, which necessitates sophisticated textures that, in turn, require substantial storage and memory resources. Thus, low-bitrate compression is crucial, especially in modern games demanding higher texture resolutions. Concurrent methodologies in texture compression predominantly employ a block-based paradigm based on color space, which inevitably leads to representational redundancies and a limited compression scope, particularly at lower bitrates. In the context of mobile devices, bandwidth during texture loading and runtime memory are major bottlenecks, making existing compression algorithms inadequate for high-resolution textures. To mitigate these limitations, we propose a novel multi-resolution texture compression scheme, Neural Block Compression (NBC), developed within the neural feature domain. Our encoding scheme is constructed on a hierarchy of multi-resolution neural feature blocks, and the key ingredient is the variable bitrates quantization scheme. It allocates higher bitrates to higher feature mip-levels and lower bitrates to lower feature mip-levels, thereby extending the concept of block compression from color domain into neural feature domain. Extensive experiments demonstrate the superior texture compression quality achieved by the proposed scheme, especially at low bitrates.

Dynamic point cloud compression (DPCC) is crucial in applications like autonomous driving and AR/VR. Current compression methods face challenges with complexity management and rate control. This paper introduces a novel dynamic coding framework that supports variable bitrate and computational complexities. Our approach includes a slimmable framework with multiple coding routes, allowing for efficient Rate-Distortion-Complexity Optimization (RDCO) within a single model. To address data sparsity in inter-frame prediction, we propose the coarse-to-fine motion estimation and compensation module that deconstructs geometric information while expanding the perceptive field. Additionally, we propose a precise rate control module that content-adaptively navigates point cloud frames through various coding routes to meet target bitrates. The experimental results demonstrate that our approach reduces the average BD-Rate by 5.81% and improves the BD-PSNR by 0.42 dB compared to the state-of-the-art method, while keeping the average bitrate error at 0.40%. Moreover, the average coding time is reduced by up to 44.6% compared to D-DPCC, underscoring its efficiency in real-time and bitrate-constrained DPCC scenarios.

The article presents and investigates, for the first time, a method for the structural synthesis of adaptive numerical-impulse functional converters with variable bit width operating in binary code. A new algorithm for their functioning is proposed, utilizing specialized parallel structures that enable efficient parallel computation at the digit and functional block levels. This significantly enhances processing speed compared to known analogs. The developed structural synthesis method is examined using the example of a control system for autonomous vehicles. The proposed methods and tools for performing arithmetic operations and computing elementary mathematical functions can serve as fundamental computational components for various functional transformations of input signals in the form of impulse streams. Numerical-impulse codes can be received from primary measurement sensors or specialized modeling devices with frequency or numerical-impulse outputs. The popularity of this method is due to the advantages of numerical-impulse or frequency-based signal representation. Specifically, signals consisting of fixed-amplitude pulses, where information is encoded in temporal or frequency parameters, exhibit high resistance to noise, shifts, and other interferences. This significantly simplifies data processing and transformation compared to other encoding types. Additionally, the transition from frequency signals to digital formats is relatively simple, making them convenient for use in sensor interfaces and control signal generation. Importantly, the developed structure allows for easy adaptation to dynamic changes in input data, ensuring a balance between accuracy, speed, and efficiency for systems that process numerical-impulse signals in real time. Preliminary practical experiments confirm the effectiveness of the proposed synthesis method in accelerating specialized integral calculations of unit impulses in measurement converters. This opens up new possibilities for developing high-performance numerical-impulse functional converters with variable bit width.

In recent years, large visual language models (LVLMs) have shown impressive performance and promising generalization capability in multi-modal tasks, thus replacing humans as receivers of visual information in various application scenarios. In this paper, we pioneer to propose a variable bitrate image compression scheme consisting of a pre-editing module and an end-to-end codec to achieve promising rate-accuracy performance for different LVLMs. In particular, instead of optimizing an adaptive pre-editing network towards a particular task or several representative tasks, we propose a new optimization strategy tailored for LVLMs, which is designed based on the representation and discrimination capability with token-level distortion and rank. The pre-editing module and the variable bitrate end-to-end image codec are jointly trained by the losses based on semantic tokens of the large model, which introduce enhanced generalization capability for various data and tasks. Experimental results demonstrate that the proposed framework could efficiently achieve much better rate-accuracy performance compared to the state-of-the-art coding standard, Versatile Video Coding. Meanwhile, experiments with multi-modal tasks have revealed the robustness and generalization capability of the proposed framework.

A low-power, fast-response transmitter based on frequency-shift keying (FSK) is presented and used in the Sub-GHz band. This transmitter uses the closed-loop modulation structure of a phase-locked loop (PLL) and maintains the constant loop bandwidth of a PLL to ensure a consistent data rate at each frequency point of Sub-GHz. A fast and accurate VCO frequency sub-band selection technology is proposed to reduce the selection time of the optimal variable capacitor array control bits of VCO, thus improving the response speed of PLL and transmitter. This transmitter is implemented in the SMIC 0.18 μm CMOS process. The measured results showed that the selection time of the optimal VCO frequency sub-band is only 3.04 us, and the Error Vector Magnitude (EVM) of the whole transmitter is 4.17% at 115 kHz data rate, meeting the wireless transmission requirements of the nodes of Internet of things.

This paper introduces two new algorithms for fault-tolerant design of multi-layered networks, both of which extend the previously published multi-layered market algorithm (MMA), by including provision of additional resources to be used during network failure events. The new algorithms are called resilient MMA (RMMA) and failure-traffic MMA (FTMMA). RMMA runs MMA iteratively and independently for each failure scenario. FTMMA treats each failure event as a type of traffic, which enables more efficient sharing of network resources. Both RMMA and FTMMA consider a range of single physical link failures and aim to maximize earnings before interest and tax (EBIT). The costs considered in the EBIT evaluation include amortized capital and operational expenditures and penalties (compensation to the customers when the service is degraded). They both focus on optimizing resource provisioning, in particular, capacity assignment, for fault-tolerant and cost-effective design of multi-layered networks. The novel aspects of RMMA and FTMMA include the incorporation of variable bit rate traffic streams in fault-tolerant multilayered network design, together with the aim to maximize EBIT. RMMA and FTMMA are validated by comparing designs with those produced by an integer linear programming benchmark for small-size networks. Numerical results show that FTMMA can more efficiently allocate capacity for failures by sharing these resources across different failure events.

End-to-end optimized Learned Image Compression (LIC) has demonstrated remarkable performance in terms of Rate-Distortion (R-D) efficiency. However, the R-D characteristics of LIC codecs remain underexplored. Previous research has attempted to investigate the R-D behavior through numerical and statistical approaches, but these methods often provide only empirical results, lacking theoretical insights. In this work, we introduce a novel methodology for studying the R-D characteristics of LIC. By rethinking the LIC paradigm from a fresh perspective, we propose a plug-and-play module, the Latent-domain Auto-Encoder (LAE). This innovative approach not only naturally leads to Variable Bit-Rate (VBR) compression, but also allows for a theoretical modeling of the R-D behavior of LIC codecs. Our findings reveal that the bit-rate is the logarithmic sum of the neurons <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">λ</sub> in our designed network’s last layer, plus a constant <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> introduced by image content, formally expressed as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">λ</sub> = Σ log <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">λ</sub> + <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i>. This insight is pivotal, as it underscores how the bit-rate can be systematically derived from the latent representations. Further analysis demonstrates that our proposed R-λ model enables effective rate control for learned image codecs, enhancing their adaptability and accuracy. Experimental results validate that our VBR method surpasses fixed-rate coding by 2.9% in terms of BD-rate. Additionally, the proposed R-λ model exhibits superior rate control performance, suggesting that it not only elucidates the underlying R-D characteristics of LIC but also significantly enhances its practical deployment in real-world applications.

In recent years, there has been a growing interest in investigating the interactions of communication and control in networked control systems (NCSs) or multiagent systems (MASs). Although extensive research has been explored on these interactions, most studies are oversimplified and idealized. Therefore, more in-depth and practical studies on communication in control systems are needed. Our study aims to explore the practical impacts of communication networks on control systems. We provide a general overhead analysis for the structure of NCSs and design an express delivery-based cost chart, referred to as an E-Chart, for the control message sizes. We found an effect in the express delivery service known as the “Rice Grain Effect,” defined as the cost of delivering a single rice grain is the same as the cost of delivering two rice grains. We apply the effect to the control overhead analysis and the E-Chart. We present the results of the E-Chart and the overhead under different message sizes and compression ratios; furthermore, we define a new metric to reflect the stability of the formation control and conduct simulations to study the impacts of message size and error on NCSs. In particular, formation control is affected by both bit error rate and message size, and the system exhibits high sensitivity to variations in bit error rate.

Data transmission security has become a critical challenge in the age of the Internet of Things (IoT) because of the increasing threats. To enhance data security in IoT-based environments, this study introduces a hybrid cryptographic and steganographic approach. Unlike cryptography, where data is encrypted to prevent unauthorized access, steganography hides data inside innocuous media. In addition to encrypting sensitive information, the proposed method hides it from potential attackers by combining these technologies. In order to achieve superior security and scalability, a Variable Least Significant Bit (VLSB) Substitution scheme is employed along with advanced cryptographic techniques. Comparing hybrid methods to traditional methods, experimental results demonstrate that the hybrid approach significantly reduces the number of altered pixels during data embedding. Smart IoT environments benefit from this dual-layer strategy by preserving data integrity and confidentiality, making them more resilient and secure.

We introduce a fuzzy proportional-integral and derivative (FPI-D) rate control algorithm designed for the variable bitrate (VBR) applications within the H.266/VVC standard. The algorithm operates at the group of picture (GOP) level to minimize unnecessary quantization parameter (QP) fluctuations at the frame level. This approach ensures stable and high visual quality in the encoded video. However, it may lead to increased bitrate variations and buffering delay while the average bitrate converges to the target bitrate in the long term and satisfies the buffer constraints. The proposed algorithm employs an FPI-D controller to compute a base QP for each GOP, considering the buffer status and current bitrate. It then utilizes a new QP cascading model to determine QP offsets for GOP frames. The proposed controller has been implemented in the H.266/VVC reference software (VTM). The rate-distortion (R-D) performance evaluation demonstrates its superiority over the default rate controller employed in the VTM software in terms of peak signal-to-noise ratio (PSNR) and structural similarity index. This achievement includes significant bitrate reductions and quality enhancements while obeying the buffering constraint. Furthermore, its R-D performance is comparable to the constant quantization parameter encoding case, emphasizing its efficiency. Moreover, the buffering delay and mean absolute gradient of PSNR measurements reveal the proposed algorithm’s superiority over the default controller in terms of visual quality. These metrics affirm that compared with the default rate controller, the proposed controller utilizes more efficient buffer space, thus preventing unnecessary QP fluctuations and undesirable PSNR variations.

Adaptive BitRate (ABR) algorithms have become increasingly prevalent in modern streaming platforms, offering users significant improvements in the Quality of Experience (QoE). With streaming providers like YouTube and Netflix shifting to high-fidelity audio formats such as stereophonic sound and Dolby Atoms, ensuring proper audio and video adaptation has become a critical aspect of modern streaming platforms. Additionally, Variable Bitrate (VBR) encoding has gained great popularity in encoding audio and video content, given its higher quality-to-bits ratio. However, the considerable variability in network bandwidth, in combination with VBR features such as significantly fluctuating audio/video chunk sizes and diverse content complexity, makes existing ABR schemes formidable to make optimal bitrate selection due to their overlook of audio adaptation or oblivious to VBR features. In this paper, we introduce a new ABR approach for <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</u>BR-based <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u>udio-aware video <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u>tr<underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</u>aming named VASE, which harnesses deep reinforcement learning (DRL) and exploits parallel computing with multiple agents to swiftly and adeptly manage fluctuations in video/audio chunk sizes, network bandwidth, and varying content complexity, all while operating without any assumptions. Besides, two variants are proposed to mitigate the download energy cost and handle audio and video content in finer granularity. Extensive trace-driven, testbed, and subjective evaluations show that our scheme surpasses existing advanced adaptation schemes regarding the overall QoE, effectively demonstrating its superiority.

The paper demonstrates the transmission of variable bit rates NRZ streams over different lengths of perfluorinated graded-index plastic optical fiber GI-POF at wavelengths of 1550 and 1310 nm. Specifically, at <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\lambda$</tex-math></inline-formula> = 1550 nm, 16, 14 and 12 Gbps of NRZ transmissions are demonstrated for 50 m GI-POF. Furthermore, at <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\lambda$</tex-math></inline-formula> = 1310 nm, 10 and 8 Gbps NRZ transmissions are demonstrated for distances of 50, 100 and 200 m. The performance is evaluated for realized transmissions in terms of received bit-error rate versus transmitted optical power and is compared to back to back performance.

In this paper, we introduce an efficient lossy coding procedure specifically tailored for handling video sequences of automotive high-dynamic range (HDR) image sensors in advanced driver-assistance systems (ADASs) for autonomous vehicles. Nowadays, mainly for security reasons, lossless compression is used in the automotive industry. However, it offers very low compression rates. To obtain higher compression rates, we suggest using lossy codecs, especially when testing image processing algorithms in software in-the-loop (SiL) or hardware-in-the-loop (HiL) conditions. Our approach leverages the high-quality VP9 codec, operating in two distinct modes: grayscale image compression for automatic image analysis and color (in RGB format) image compression for manual analysis. In both modes, images are acquired from the automotive-specific RCCC (red, clear, clear, clear) image sensor. The codec is designed to achieve a controlled image quality and state-of-the-art compression ratios while maintaining real-time feasibility. In automotive applications, the inherent data loss poses challenges associated with lossy codecs, particularly in rapidly changing scenes with intricate details. To address this, we propose configuring the lossy codecs in variable bitrate (VBR) mode with a constrained quality (CQ) parameter. By adjusting the quantization parameter, users can tailor the codec behavior to their specific application requirements. In this context, a detailed analysis of the quality of lossy compressed images in terms of the structural similarity index metric (SSIM) and the peak signal-to-noise ratio (PSNR) metrics is presented. With this analysis, we extracted some codec parameters, which have an important impact on preservation of video quality and compression ratio. The proposed compression settings are very efficient: the compression ratios vary from 51 to 7765 for grayscale image mode and from 4.51 to 602.6 for RGB image mode, depending on the specified output image quality settings. We reached 129 frames per second (fps) for compression and 315 fps for decompression in grayscale mode and 102 fps for compression and 121 fps for decompression in the RGB mode. These make it possible to achieve a much higher compression ratio compared to lossless compression while maintaining control over image quality.

SummaryThe expanding popularity of Voice over WiFi (VoWiFi) necessitates a concerted effort to identify novel ways to increase VoWiFi cell capacity. The primary objective of this study is to increase the capacity of VoWiFi cells by means of frame aggregation of aggregate MAC protocol data unit (A‐MPDU) for variable bit rate (VBR) traffic. Taking into account Arbitration Inter‐frame Spacing (AIFS), Compressed RTP (cRTP) and A‐MPDU frames, we devised a formula to calculate an approximate number of concurrent VoWiFi users that can coexist with no detriment to the quality‐of‐service (QoS) of existing VoWiFi calls over the Wireless Fidelity (WiFi) standards. Here, we used AIFS to determine the channel's health before sending Voice over WiFi data and Short Inter‐frame Spacing (SIFS) to transfer frames such as Request To Send (RTS)/Clear To Send (CTS) and Acknowledgement (ACK). We have used our suggested model to analyse the capacity of VoWiFi cells in IEEE 802.11b/g/n/ac/ax/be Wireless Local Area Network (WLAN)s with VBR traffic utilising DCF Inter‐frame Spacing (DIFS) and AIFS. For IEEE 802.11b/g/n/ac/ax/be, we also determined the most number of MAC protocol data unit (MPDU)s that may be combined into a single A‐MPDU. We have also studied the impact of voice packet retransmission on the cell capacity of a WLAN standard that offers VoWiFi service while taking A‐MPDU method into account. We have compared the results gained using IEEE 802.11be with earlier WLAN standards like IEEE 802.11b/g/n/ac/ax considering the constant bit rate (CBR) and VBR traffics.