LiveRender

Multimedia content adaption strategies are becoming increasingly important for effective video streaming over the actual heterogeneous networks. Thus, evaluation frameworks for adaptive video play an important role in the designing and deploying process of adaptive multimedia streaming systems. This paper describes a novel simulation framework for rate-adaptive video transmission using the Scalable Video Coding standard (H.264/SVC). Our approach uses feedback information about the available bandwidth to allow the video source to select the most suitable combination of SVC layers for the transmission of a video sequence. The proposed solution has been integrated into the network simulator NS-2 in order to support realistic network simulations. To demonstrate the usefulness of the proposed solution we perform a simulation study where a video sequence was transmitted over a three network scenarios. The experimental results show that the Adaptive SVC scheme implemented in our framework provides an efficient alternative that helps to avoid an increase in the network congestion in resource-constrained networks. Improvements in video quality, in terms of PSNR (Peak Signal to Noise Ratio) and SSIM (Structural Similarity Index) are also obtained.

The Ultra High-Definition (UHD) standard introduces improvements in video quality across several dimensions. Although the first to be adopted commercially was the increase in resolution, features such as high frame rate and dynamic range, wide color gamut, and increased bit-depth offer important gains in the quality of experience of the end user. Nevertheless, each of these improvements comes with an associated cost in terms of storage, bandwidth, and energy consumption. In this paper, we first present key studies for evaluating the energy impact of streaming video and associated models. We then aim to quantify this cost for each UHD dimension, considering the distinct elements of an example streaming video chain and using concrete scenarios that combine different UHD features, an end-to-end model is employed to provide a comparative analysis. This allows us to evaluate the impact of each UHD characteristic and aid in guiding workflow choices for UHD content production and distribution.

This paper proposes a device-to-device (D2D) communications based scarcity-aware two-stage multicast mechanism for delivering video streaming, in which the Base Station (BS) and D2D perform cooperative retransmissions to improve the mobile video quality and relieve the load of BS as well. In prior works, after BS's one-round multicast, the clients directly resort to D2D communications to obtain the missing frames by forming social groups. However, after the first-round multicast of BS, it is possible that some frames have not been correctly received by most of the clients, especially when the channel conditions are not good. That is, there are scarce frames in the network and the clients have very limited copies of them. In this case, it is inefficient for the clients to form social groups and directly exchange frames through D2D communications. Instead, this paper proposes an adaptive two-stage multicast mechanism: the BS first multicast video packets to the clients who request the same video services, after which the clients collect the information of missing frames and send it to the BS. Then the BS evaluates the scarcity of frames and re-multicast the scarce frames, if any, to the clients. After the BS's second-round multicast, the clients can then form groups to perform D2D communications so as to obtain the remaining missing packets from each other. Numerical studies show that the proposed simple scarcity-aware two-stage multicast mechanism can achieve about 5% video quality improvement than the existing one-stage multicast mechanism and have the potential to enhance the downlink capacity of the future 5G wireless network.

Variable Bitrate (VBR) video encoding can significantly improve the quality-of-experience (QoE) of viewing users due to its capability to provide much higher quality-to-bits ratio compared to Constant Bitrate (CBR) video encoding. However, the streaming of VBR-encoded videos suffers from large variance of video chunk size, which may directly result in frequent rebuffering if not properly handled. In this paper, we propose a novel neural-enhanced adaptive streaming framework for VBR-encoded videos called PreSR, which performs selective prefetching of video chunks to achieve a higher QoE for viewers. The design of PreSR is motivated by an important observation obtained from our measurement, namely, the video quality improvement and bandwidth savings brought by neural enhancement are more pronounced for low-resolution video chunks with complex scenes. By taking the above fact and the time required for neural enhancement into account, we formulate the problem into an optimization problem. Given that the problem is NP-hard, we design the PreSR framework, which is based on the model predictive control theory and also considers key features of VBR-encoded videos. PreSR parallelizes the download of video chunks and model inference processes to fully utilize the available compute resources. Finally, we conduct extensive experiments with real traces, and the results show that PreSR outperforms the state-of-the-art algorithms with an improvement up to 11.25% in terms of the average QoE.

Cloud gaming enables people playing graphically intensive games from their less powerful, or even outdated computing devices. It is challenging to realize cloud gaming as it requires minimal latency in server-side processing, rendering and streaming, which are expensive in terms of resource requirements, e.g., powerful GPU servers. Commercial gaming providers, e.g., Google Stadia, Amazon Luna, etc., hardly disclose any information on how they optimize gaming performance and cloud cost. In this work, we aim to investigate resource cost optimization for such cloud gaming systems. In contrast to previous work which have been focusing more on theoretical approaches, we deliver a fully functional, cost-optimized cloud-native gaming system, called CloudNPlay, implemented entirely on AWS Lambda, EC2 and other AWS services. We have conducted extensive evaluations on the gaming performance including latency and frame rates; as well as cloud resource cost reduction. The results indicate that games hosted on CloudNPlay are highly playable. More importantly, CloudNPlay reduces the resource cost by around 24% compared to a non-optimized deployment. We plan to open-source CloudNPlay to facilitate further research in cloud gaming.

Virtual reality (VR) 360 video applications typically require bandwidths of at least 15–20 Mbits/s while providing limited video quality. This is because users only observe a small part of the VR panorama at any time: about 12%–15%. But, the full panorama needs to be provided to users on a continuous basis. This paper summarizes the state of the art in VR streaming, detailing how tiled VR streaming technology can reduce bandwidth requirements by an order of magnitude while improving the video quality. This is achieved by dividing a VR panorama into tiles and streaming only those tiles that users actually look at while wearing head-mounted displays (HMDs). This solution is based on HEVC tiling features as well as the DASH extension. A major challenge for any viewport-dependent VR streaming technology is motion-to-photon delay. Using a combination of selective tile transfer, a low-resolution base layer, smart packaging, and very low-latency protocols, tiled VR streaming can offer extremely low motion-to-photon delay even when streaming from existing content delivery networks. This paper will conclude by showing the tradeoffs between quality and bitrate when tiling from 4K or 8K sources to produce HD and UHD experiences, respectively, on HMDs, based on field trials and realistic use cases.

Cloud gaming systems host the game in the cloud, and stream players' gaming videos to the terminals in the form of encode video frames. To address the high bandwidth issue of real-time gaming video transmission, a cloudlet-assisted multiplayer cloud gaming system was proposed to encourage cooperative video sharing via a secondary ad-hoc network, on the purpose of exploiting the similarities of video frames among multiple players in a same game. However, the video cooperative sharing among players also introduces fairness problems. In this paper, we complete the ad hoc cloudlet-assisted cloud gaming system by further considering the mobility of terminal devices and the diversity of network quality for distinct players. With mathematical formulation, we study the players' behavior in cooperative sharing patterns and propose a reputation-based multiplayer fairness scheme in terms of frame encoding. Experimental results illustrate the impact of mobility on the system performance and evaluate that the proposed solution provides better fairness gaming ecosystem compared to the existing platform.

Cloud gaming is promising to provide high-quality game services by outsourcing game execution to cloud so that users can access games via thin clients (e.g., smartphones or tablets). However, existing cloud gaming systems su er from low GPU utilization in the virtualized environment. Moreover, GPU resources are scheduled in units of virtual machines (VMs) and this kind of coarse-grained scheduling at the VM-level fails to fully exploit GPU processing capacity. In this paper, we present ShareRender, a cloud gaming sys- tem that o oads graphics workloads within VMs directly to GPUs, bypassing GPU virtualization. For each game running in a VM, ShareRender starts a graphics wrapper to intercept frame rendering requests and assign them to render agents responsible for frame rendering on GPUs. Thanks to the exible workload assignment among multiple render agents, ShareRender enables ne-grained resource sharing at the frame-level to signi cantly improve GPU utilization. Further more, we design an online algorithm to determine workload assignment and migration of render agents, which considers the tradeo between minimizing the number of active server and low agent migration cost. We conduct experiments on real deployment and trace-driven simulations to evaluate the performance of ShareRender under di erent system settings. The results show that ShareRender outperforms the existing video-streaming-based cloud gaming system by over 4 times.

The transmission of video data is a major part of traffic on today's Internet. Since the Internet is a highly dynamic environment, quality adaptation is essential in matching user device resources with the streamed video quality. This can be achieved by applying mechanisms that follow the Scalable Video Coding (SVC) standard, which enables scalability of the video quality in multiple dimensions. In SVC-based streaming, adaptation decisions have long been driven by Quality of Service (QoS) metrics, such as throughput. However, these metrics do not well match the way human users perceive video quality. Therefore, in this paper, the classical SVC-based video streaming approach is expanded to consider Quality of Experience (QoE) for adaptation decisions. The video quality is assessed using existing objective techniques with a high correlation to the human perception. The approach is evaluated in context of a P2P-based Video-on-Demand (VoD) system and shows that by making peers favor always layers with a high estimated QoE but not necessarily high bandwidth requirements, the performance of the entire system can be enhanced in terms of playback delay and SVC video quality by up to 20%. At the same time, content providers are able to reduce up to 60 of their server costs, compared to the classical QoS-based approach.

Real-time data transmission through telepresence surgery consideration as an important issue due to using low video quality transmission techniques for transferring video data. Many types of research have been conducted on real-time video transmission but very few have been focused on surgical telepresence and improving video quality during the surgery. The main aim of this research is used to improve the video quality by improving the path quality which the video data is transferred. The proposed system consists of a Transmission Control Protocol (TCP) Friendly for controlling congestion while selecting the best quality of path for transmitting the video data. It selects the best quality of path that is less likely to be affected by network impairments and provides a higher priority to the TCP friendly packets than the non-TCP friendly packets. Results for the end video quality has been improved by 4.2 dB compared to the state of art based on path quality, PSNR and Packet Traffic are based on congestion and also the processing time has been improved by 9 ~ 13 Frames per Second on average against 5 ~ 9 Frames per Second of the state of art. The proposed system makes sure that the packets are sent through the best path and that is done by giving the TCP friendly packets higher priority than non-TCP friendly packets, also considers the available bandwidth, loss rates and other parameters that are utilized in other systems. In addition to the state of art, it adds congestion factor and makes sure that only the path with minimum distortion was used to send maximum data so that the receiver site can handle the transmitted data.

Tele-training in surgical education has not been effectively implemented. There is a stringent need for a high transmission rate, reliability, throughput, and reduced distortion for high-quality video transmission in the real-time network. This work aims to propose a system that improves video quality during real-time surgical tele-training. The proposed approach aims to minimise the video frame’s total distortion, ensuring better flow rate allocation and enhancing the video frames’ reliability. The proposed system consists of a proposed algorithm for Enhancing Video Quality, Distorting Minimization, Bandwidth efficiency, and Reliability Maximization called (EVQDMBRM) algorithm. The proposed algorithm reduces the video frame’s total distortion. In addition, it enhances the video quality in a real-time network by dynamically allocating the flow rate at the video source and maximizing the transmission reliability of the video frames. The result shows that the proposed EVQDMBRM algorithm improves the video quality with the minimized total distortion. Therefore, it improves the Peak Signal to Noise Ratio (PSNR) average by 51.13 dB against 47.28 dB in the existing systems. Furthermore, it reduces the video frames processing time average by 58.2 milliseconds (ms) against 76.1, and the end-to-end delay average by 114.57 ms against 133.58 ms comparing to the traditional methods. The proposed system concentrates on minimizing video distortion and improving the surgical video transmission quality by using an EVQDMBRM algorithm. It provides the mechanism to allocate the video rate at the source dynamically. Besides that, it minimizes the packet loss ratio and probing status, which estimates the available bandwidth.

Objective: YouTube videos have an important role in public health. This study aimed to assess the content, quality, and reliability of hemophilia videos on the YouTube platform. Methods: The keywords, namely, hemophilia, hemophilia factor, hemophilia inhibitors, and hemophilia treatment were used to identify videos on YouTube. The Global Quality Scale and the modified DISCERN questionnaire were used to assess the quality and reliability of videos, respectively. The videos were compared based on their quality and uploaders (physician, healthcare organization, company, patient, and independent users). Results: A total of 140 videos were reviewed for the study. There were 65 high, 51 moderate, and 24 low-quality videos. The physicians uploaded more videos and high-quality videos than others, with a few videos uploaded by patients and healthcare organizations. The like ratio was different for high-quality videos (P < .05), other video characteristics were similar (P > .05). The median Global Quality Scale and DISCERN scores of all videos were the same (3 points) and DISCERN scores increased with improved video quality (P < .05). Various significant differences were found in video characteristics between uploader groups (P < .05). The Global Quality Scale score of pysicians was significantly different when compared to other groups (P < .05). The DISCERN scores of physician videos were different compared to the patient and independent users-sourced videos (P = .006 and P = .001) and DISCERN scores of patient videos were also different from those of healthcare organization videos (P = .004). Conclusion: YouTube can be considered as a good source of information on hemophilia for people with hemophilia and may improve their self-management skills. Cite this article as: Gönen T, Usgu S, Akbayram S, Yakut Y. Is YouTube a reliable source of medical information about hemophilia? Arch Health Sci Res. 2022;9(2):86-91.

The emerging 60-GHz multigigabits per second wireless technology enables the streaming of high-quality “uncompressed” video, which has been impossible with other existing wireless technologies. To support such a resource-hungry uncompressed video streaming service with limited wireless resources, it is necessary to design efficient link adaptation policies selecting suitable transmission rates for the 60-GHz wireless channel environment, thus optimizing video quality and resource management. For proper design of the link adaptation policies, we propose a new metric, called expected peak signal-to-noise ratio (ePSNR) , to numerically estimate the video streaming quality. By using the ePSNR as a criterion, we propose two link adaptation policies with different objectives considering unequal error protection (UEP). The proposed link adaptation policies attempt to 1) maximize the video quality for given wireless resources, or 2) minimize the required wireless resources, while meeting the video quality. From the link adaptation policies, we provide a distributed resource management scheme for multiple users to maintain satisfactory video streaming quality. Our extensive simulation results demonstrate that the newly proposed variable, i.e., ePSNR, well represents the level of video quality. It is also shown that the proposed link adaptation policies can enhance the resource efficiency while achieving acceptable quality of the video streaming.

With the rapid advancement in many multimedia applications, such as video gaming, computer vision applications, and video streaming and surveillance, video quality remains an open challenge. Despite the existence of the standardized video quality as well as high definition (HD) and ultrahigh definition (UHD), enhancing the quality for the video compression standard will improve the video streaming resolution and satisfy end user's quality of service (QoS). Versatile video coding (VVC) is the latest video coding standard that achieves significant coding efficiency. VVC will help spread high-quality video services and emerging applications, such as high dynamic range (HDR), high frame rate (HFR), and omnidirectional 360-degree multimedia compared to its predecessor high efficiency video coding (HEVC). Given its valuable results, the emerging field of deep learning is attracting the attention of scientists and prompts them to solve many contributions. In this study, we investigate the deep learning efficiency to the new VVC standard in order to improve video quality. However, in this work, we propose a wide-activated squeeze-and-excitation deep convolutional neural network (WSE-DCNN) technique-based video quality enhancement for VVC. Thus, the VVC conventional in-loop filtering will be replaced by the suggested WSE-DCNN technique that is expected to eliminate the compression artifacts in order to improve visual quality. Numerical results demonstrate the efficacy of the proposed model achieving approximately −2.85%, −8.89%, and −10.05% BD-rate reduction of the luma (Y) and both chroma (U, V) components, respectively, under random access profile.

For the cloud computing based on software-defined networks (SDNs), a larger amount of data is collected to cloud for analysis, which will cause the larger amount of redundancy data and longer service response time due to the capacity-limited Internet. To solve this problem, a novel service orchestration and data aggregation framework (SODA) is proposed, which can orchestrate data as services and aggregate data packets to reduce data redundancy and service response delay. In SODA, the network is divided into three layers. 1) Data centers layer (DCL). Data centers (DCs) release software with a specific function to all devices in the network, devices orchestrate data as services and aggregate data packets using software to reduce service response delay. 2) Middle routing layer (MRL). The routing path of data packets in this layer is adjusted according to the correlation of data packets and routing distance. The correlation of data packets is higher and routing distance is short, the probability that data packets are transmitted along the same routing path is higher to reduce redundancy data. 3) Vehicle network layer (VNL). Mobile vehicles are used to transmit data packets and services among devices. A series of experiments and simulation is conducted. The results illustrate that the proposed scheme has better performance compared with the traditional scheme.