Demands for uplink on mobile networks are increasing with the rapid development of social media platforms, 4K/8K content creation, IoT applications, and Fixed Wireless Access (FWA) broadband. As a result, Uplink MIMO (UL-MIMO) and Uplink Carrier Aggregation (UL-CA) have been widely deployed for the first time on commercial 5G networks. UL-MIMO enables the transmission of two data streams on one frequency band in strong RF conditions, theoretically doubling throughput and efficiency. On the other hand, UL-CA allows for simultaneous upload on greater channel widths, allowing more resources to be assigned to a single UE for higher throughput.In the United States, T-Mobile USA, a mobile network operator (MNO), has deployed network-wide 5G Standalone (SA), along with UL-MIMO on Time Division Duplex (TDD) band n41 and UL-CA between TDD and Frequency Division Duplex (FDD) NR bands. In this paper, the uplink throughput performance of UL-MIMO and UL-CA will be evaluated on the commercial T-Mobile 5G network on a variety of RF environments and modes of transportation. It was found that, even with the efficiency gains, UL-MIMO yields slower uplink throughput in most scenarios. However, in stronger RF conditions, UL-MIMO can provide an adequate user experience, so capacity can be conserved by reserving UL-CA for UE in weaker RF conditions.

The 5G New Radio (NR) standard introduces new frequency bands allocated in Frequency Range 2 (FR2) to support enhanced Mobile Broadband (eMBB) in congested environments and enables new use cases such as Ultra-Reliable Low Latency Communication (URLLC). The 3GPP introduced 256QAM support for FR2 frequency bands to further enhance downlink capacity. However, sustaining 256QAM on FR2 in practical environments is challenging due to strong path loss and susceptibility to distortion. While 256QAM can improve theoretical throughput by 33%, compared to 64QAM, and is widely adopted in FR1, its real-world impact when utilized in FR2 is questionable, given the significant path loss and distortions experienced in the FR2 range. Additionally, using higher modulation correlates to higher BLER, increased instability, and retransmission. Moreover, 256QAM also utilizes a different MCS table defining the modulation and code rate at different Channel Quality Indexes (CQI), affecting the UE's link adaptation behavior. This paper investigates the real-world performance of 256QAM utilization on FR2 bands in two countries, across three RAN manufacturers, and in both NSA (EN-DC) and SA (NR-DC) configurations, under various scenarios, including open-air plazas, city centers, footbridges, train station platforms, and stationary environments. The results show that 256QAM provides a reasonable throughput gain when stationary but marginal improvements when there is UE mobility while increasing the probability of NACK responses, increasing BLER, and the number of retransmissions. Finally, MATLAB simulations are run to validate the findings as well as explore the effect of the recently introduced 1024 QAM on FR2.

While Time Division Duplexing (TDD) 5G New Radio (NR) networks offers higher downlink throughput due to the utilization of the middle frequency band, the uplink performance is negatively impacted due to higher path loss associated with higher frequencies, which degrade the users’ QoE in less optimal conditions. With the growing demand for high performance uplink throughput from novel applications such as Metaverse, Internet of Things (IoTs) and Smart City, 3GPP introduced High Power User Equipment (HPUE) on 5G TDD bands, allowing UEs to utilize more than 23 dBm of power for transmission to improve throughput, QoE, and reliability, especially at the cell edges. In this paper, the performance of HPUE is evaluated in the urban area on a commercial 5G network in terms of Uplink Throughput, Modulation Efficiency, Re-transmission Rate (ReTx Rate), and Power Consumption in both Standalone (SA) and Non-Standalone (NSA) modes. Through modem firmware modification, the performance is also compared across different power classes and antenna configurations.

The exponential growth of User-Generated Content (UGC), especially High-Definition (HD) live video streaming, places a significant demand on the uplink capabilities of mobile networks. To address this, the 5G New Radio (NR) standard introduced key uplink enhancements, including Uplink Multi-Input Multi-Output (UL-MIMO) and Uplink 256QAM, to improve throughput and spectral efficiency. However, while the benefits of these features for raw data rates are well-documented, their practical impact on real-time applications like live-streaming is not yet well understood. This paper investigates the performance of UL-MIMO and UL-256QAM for HD live-streaming over a commercial 5G network using the Real-Time Messaging Protocol (RTMP). To ensure a fair assessment, we conduct a comparative analysis by modifying the modem firmware of commercial User Equipment (UE), allowing these features to be selectively enabled and disabled on the same device. Performance is evaluated based on key metrics, including dropped video frames and connection stability. Furthermore, this study analyzes 5G Radio Frequency (RF) parameters to quantify the spectral efficiency impact, specifically examining metrics derived from the Channel State Information (CSI) framework, including Reference Signal Received Power (CSI-RSRP), Reference Signal Received Quality (CSI-RSRQ), and Signal-to-Interference-plus-Noise Ratio (CSI-SINR).

The demand for high-quality, real-time video streaming has grown exponentially, with 4K Ultra High Definition (UHD) becoming the new standard for many applications such as live broadcasting, TV services, and interactive cloud gaming. This trend has driven the integration of dedicated hardware encoders into modern Graphics Processing Units (GPUs). Nowadays, these encoders support advanced codecs like HEVC and AV1 and feature specialized Low-Latency and Ultra Low-Latency tuning, targeting end-to-end latencies of <2 seconds and <500 ms, respectively. As the demand for such capabilities grows toward the 6G era, a clear understanding of their performance implications is essential. In this work, we evaluate the low-latency encoding modes on GPUs from NVIDIA, Intel, and AMD from both Rate-Distortion (RD) performance and latency perspectives. The results are then compared against both the normal-latency tuning of hardware encoders and leading software encoders. Results show hardware encoders achieve significantly lower E2E latency than software solutions with slightly better RD performance. While standard Low-Latency tuning yields a poor quality-latency trade-off, the Ultra Low-Latency mode reduces E2E latency to 83 ms (5 frames) without additional RD impact. Furthermore, hardware encoder latency is largely insensitive to quality presets, enabling high-quality, low-latency streams without compromise.

Packet losses in streaming 3D point clouds with V-PCC over RTP significantly impact reconstruction quality, affecting users’ quality of experience. Despite this, practical error concealment methodologies are not yet widely researched, as previous 3D-based methods suffer from real-time performance and restrict coding modes to all-intra only. In this work, we evaluate various state-of-the-art 2D video interpolation and extrapolation works as candidates for the real-time V-PCC error concealment task in the video domain. We show that although their effectiveness varies with the regularity of generated patches or V-PCC video types, they can run in real-time, significantly faster than 3D methods, and can perform effectively on V-PCC attribute loss and temporally consistent patch videos.

Many GPUs have incorporated hardware-accelerated video encoders, which allow video encoding tasks to be offloaded from the main CPU and provide higher power efficiency. Over the years, many new video codecs such as H.265/HEVC, VP9, and AV1 were added to the latest GPU boards. Recently, the rise of live video content such as VTuber, game live-streaming, and live event broadcasts, drives the demand for high-efficiency hardware encoders in the GPUs to tackle these real-time video encoding tasks, especially at higher resolutions such as 4K/8K UHD. In this paper, RD performance, encoding speed, as well as power consumption of hardware encoders in several generations of NVIDIA, Intel GPUs as well as Qualcomm Snapdragon Mobile SoCs were evaluated and compared to the software counterparts, including the latest H.266/VVC codec, using several metrics including PSNR, SSIM, and machine-learning based VMAF. The results show that modern GPU hardware encoders can match the RD performance of software encoders in real-time encoding scenarios, and while encoding speed increased in newer hardware, there is mostly negligible RD performance improvement between hardware generations. Finally, the bitrate required for each hardware encoder to match YouTube transcoding quality was also calculated.

Transmission latency significantly affects users’ quality of experience in real-time interaction and actuation. As latency is principally inevitable, video prediction can be utilized to mitigate the latency and ultimately enable zero-latency transmission. However, most of the existing video prediction methods are computationally expensive and impractical for real-time applications. In this work, we therefore propose real-time video prediction towards the zero-latency interaction over networks, called IFRVP (Intermediate Feature Refinement Video Prediction). Firstly, we propose three training methods for video prediction that extend frame interpolation models, where we utilize a simple convolution-only frame interpolation network based on IFRNet. Secondly, we introduce ELAN-based residual blocks into the prediction models to improve both inference speed and accuracy. Our evaluations show that our proposed models perform efficiently and achieve the best trade-off between prediction accuracy and computational speed among the existing video prediction methods. A demonstration movie is also provided at http://bit.ly/IFRVPDemo.

While 5G New Radio (NR) networks offer significant uplink throughput improvements, these gains are primarily realized when User Equipment (UE) connects to high-frequency millimeter wave (mmWave) bands. The growing demand for uplink-intensive applications, such as real-time UHD 4K/8K video streaming and Virtual Reality (VR)/Augmented Reality (AR) content, highlights the need for accurate uplink throughput prediction to optimize user Quality of Experience (QoE). In this paper, we introduce UplinkNet, a compact neural network designed to predict future uplink throughput using past throughput and RF parameters available through the Android API. With a model size limited to approximately 4,000 parameters, UplinkNet is suitable for IoT and low-power devices. The network was trained on real-world drive test data from commercial 5G Standalone (SA) networks in Tokyo, Japan, and Bangkok, Thailand, across various mobility conditions. To ensure practical implementation, the model uses only Android API data and was evaluated on unseen data against other models. Results show that UplinkNet achieves an average prediction accuracy of 98.9% and an RMSE of 5.22 Mbps, outperforming all other models while maintaining a compact size and low computational cost.

While Uplink 256QAM (UL-256QAM) has been introduced since 2016 as a part of 3GPP Release 14, the adoption was quite poor as many Radio Access Network (RAN) and User Equipment (UE) vendors didn't support this feature. With the introduction of 5G, the support of UL-256QAM has been greatly improved due to a big re-haul of RAN by Mobile Network Operators (MNOs). However, many RAN manufacturers charge MNOs for licenses to enable UL-256QAM per cell basis. This led to some MNOs hesitating to enable the feature on some of their gNodeB or cells to save cost. Since it's known that 256QAM modulation requires a very good channel condition to operate, but UE has a very limited transmission power budget. In this paper, 256QAM utilization, throughput and latency impact from enabling UL-256QAM will be evaluated on commercial 5G Standalone (SA) networks in two countries: Japan and Thailand on various frequency bands, mobility characteristics, and deployment schemes. By modifying the modem firmware, UL-256QAM can be turned off and compared to the conventional UL-64QAM. The results show that UL-256QAM utilization was less than 20% when deployed on a passive antenna network resulting in an average of 8.22% improvement in throughput. However, with Massive MIMO deployment, more than 50% utilization was possible on commercial networks. Furthermore, despite a small uplink throughput gain, enabling UL-256QAM can lower the latency when the link is fully loaded with an average improvement of 7.97 ms in TCP latency observed across various test cases with two TCP congestion control algorithms.

All 3GPP-compliant commercial 5G New Radio (NR)-capable UEs on the market are equipped with$4\times 4$MIMO support for Mid-Band frequencies$( > 1.7\text{GHz})$and above, enabling up to rank 4 MIMO transmission. This doubles the theoretical throughput compared to rank 2 MIMO and also improves reception performance. However, 4x4 MIMO support on low-band frequencies$(< 1\text{GHz})$is absent in every commercial UEs, with the exception of the Xperia 1 flagship smartphones manufactured by Sony Mobile and the Xiaomi 14 Pro as of January 2024. The reason most manufacturers omit$4\times 4$MIMO support for low-band frequencies is likely due to design challenges or relatively small performance gains in real-world usage due to the lack of 4T4R deployment on low-band by mobile network operators around the world. In Thailand, 4T4R deployment on the b28/n28 (APT) band is common on True-H and dtac networks, enabling 4x4 MIMO transmission on supported UEs. In this paper, the real-world 4x4 MIMO performance on the b28/n28 (APT) band will be investigated by evaluating the reliability test under different signal conditions and the maximum throughput test by evaluating the performance under optimal conditions, using the Sony Xperia 1 III and the Sony Xperia 1 IV smartphone. Devices from other manufacturers are also used in the experiment to investigate the performance with 2Rx antennas for comparison. Through firmware modifications, the Sony Xperia 1 III and IV can be configured to use only 2 Rx ports on low-band, enabling the collection of comparative 2 Rx performance data as a reference.

Entropy coding is essential to data compression, image and video coding, etc. The Range variant of Asymmetric Numeral Systems (rANS) is a modern entropy coder, featuring superior speed and compression rate. As rANS is not designed for parallel execution, the conventional approach to parallel rANS partitions the input symbol sequence and encodes partitions with independent codecs, and more partitions bring extra overhead. This approach is found in state-of-the-art implementations such as DietGPU. It is unsuitable for content-delivery applications, as the parallelism is wasted if the decoder cannot decode all the partitions in parallel, but all the overhead is still transferred.

Due to the recent demand for huge Uplink through-put on Mobile networks driven by the rapid development of social media platforms, UHD 4K/8K video, and VR/AR contents, Uplink MIMO (UL-MIMO) has now been deployed on commercial 5G networks with reasonable availability of supported User Equipment (UE) for consumers. By utilizing up to 2 Tx antenna ports, UL-MIMO-capable UE promised to achieve up to two times the uplink throughput in ideal conditions, while providing improved uplink performance over UE with 1Tx in challenging conditions.In Japan, SoftBank, one of the carriers, introduced 5G Standalone (SA) services for the Fixed Wireless Access (FWA) application back in October 2021. Mobile services were commenced in May 2022, which provide UL-MIMO for supported UE on C-Band or Band n77 (3.7 GHz). In this paper, the uplink performance of UL-MIMO-capable UE will be compared against the conventional UL-1Tx UE on trains, which is the most popular method of transportation for the Japanese. The results show that UL-MIMO-capable UE delivers an average of 19.8% better throughput on moving trains with up to 33.5% in the more favorable signal conditions. A moderate relationship between downlink 5G NR SS-RSRP and uplink throughput also has been observed.

While the rollout of the fifth-generation mobile network (5G) is underway across the globe with the intention to deliver 4K/8K UHD videos, Augmented Reality (AR), and Virtual Reality (VR) content to the mass amounts of users, the coverage and throughput are still one of the most significant issues, especially in the rural areas, where only 5G in the low-frequency band are being deployed. This called for a highperformance adaptive bitrate (ABR) algorithm that can maximize the user quality of experience given 5G network characteristics and data rate of UHD contents.Recently, many of the newly proposed ABR techniques were machine-learning based. Among that, Pensieve is one of the state-of-the-art techniques, which utilized reinforcement-learning to generate an ABR algorithm based on observation of past decision performance. By incorporating the context of the 5G network and UHD content, Pensieve has been optimized into Pensieve 5G. New QoE metrics that more accurately represent the QoE of UHD video streaming on the different types of devices were proposed and used to evaluate Pensieve 5G against other ABR techniques including the original Pensieve. The results from the simulation based on the real 5G Standalone (SA) network throughput shows that Pensieve 5G outperforms both conventional algorithms and Pensieve with the average QoE improvement of 8.8% and 14.2%, respectively. Additionally, Pensieve 5G also performed well on the commercial 5G NR-NR Dual Connectivity (NR-DC) Network, despite the training being done solely using the data from the 5G Standalone (SA) network.

5G Standalone (SA) is the goal of the 5G evolution, which aims to provide higher throughput and lower latency than the existing LTE network. One of the main applications of 5G is the real-time distribution of Ultra High-Definition (UHD) content with a resolution of 4K or 8K. In Q2/2021, Advanced Info Service (AIS), the biggest operator in Thailand, launched 5G SA, providing both 5G SA/NSA service nationwide in addition to the existing LTE network. While many parts of the world are still in process of rolling out the first phase of 5G in Non-Standalone (NSA) mode, 5G SA in Thailand already covers more than 76% of the population. In this paper, UHD video will be a real-time live streaming via MPEG-DASH over different mobile network technologies with minimal buffer size to provide the lowest latency. Then, performance such as the number of dropped segments, MAC throughput, and latency are evaluated in various situations such as stationary, moving in the urban area, moving at high speed, and also an ideal condition with maximum SINR. It has been found that 5G SA can deliver more than 95% of the UHD video segment successfully within the required time window in all situations, while 5G NSA produced mixed results depending on the condition of the LTE network. The result also reveals that the LTE network failed to deliver more than 20 % of the video segment within the deadline, which shows that 5G SA is absolutely necessary for low-latency UHD video streaming and 5G NSA may not be good enough for such task as it relies on the legacy control signal.