Video Streaming Approach Research Articles

Deep learning based enhanced secure emergency video streaming approach by leveraging blockchain technology for Vehicular AdHoc 5G Networks

VANET is a category of MANET that aims to provide wireless communication. It increases the safety of roads and passengers. Millions of people lose their precious lives in accidents yearly, millions are injured, and others incur disability daily. Emergency vehicles need clear roads to reach their destination faster to save lives. Video streaming can be more effective as compared to textual messages and warnings. To address this issue, we proposed a methodology to use visual sensors, cameras, and OBU to record emergency videos. Initially, the frames are detected. After re-recording, the frames detection algorithm detects the specific event from the video frames. Blockchain encrypts an emergency or specific event using hashing algorithms in the second layer of our proposed framework. In the third layer of the proposed methodology, encrypted video is broadcast with the help of 5G wireless technology to the connected nodes in the VANET. The dataset used in this research comprises up to 72 video sequences averaging about 120 seconds per video. All videos have different traffic conditions and vehicles. The ResNet-50 model is used for the feature extraction process of extracted frames. The model is trained using Tensorflow and Keras deep learning models. The Elbow method finds the optimal K number for the K Means model. This data is split into training and testing. 70% is reserved for training the support vector machine (SVM) model and test datasets, while 30%. 98% accuracy is achieved with 98% precision and 99% recall as results for the proposed methodology.

QoE-Driven Cross-Layer Bitrate Allocation Approach for MEC-Supported Adaptive Video Streaming

QoE-Driven Cross-Layer Bitrate Allocation Approach for MEC-Supported Adaptive Video Streaming

A learning-based approach for video streaming over fluctuating networks with limited playback buffers

In recent times, the surge in applications like social networking and online learning platforms has led to a substantial rise in on-demand video streaming, thus ensuring a seamless user experience holds paramount importance, particularly given the dynamic nature of network conditions. Moreover, numerous service providers are embracing smaller buffer sizes, aiming to reduce bandwidth inefficiencies due to the possibility of users ending video sessions prematurely. However, this transition presents a significant challenge for conventional adaptive bitrate (ABR) algorithms, as they grapple with the task of harmonizing low stalling time, high playback bitrate, and the constraint of a minimized buffer size. In this study, we introduce a novel ABR approach, L2-ABR, which leverages self-play reinforcement learning to address these complexities. Unlike conventional reward-engineering learning-based ABR strategies that update gradients to maximize linear reward functions, L2-ABR treats video streaming as a fundamental objective and trains neural networks (NNs) with explicit requirements tailored to video streaming with small playback buffers. Through an extensive series of trace-driven experiments, we showcase that L2-ABR outperforms existing methods, effectively striking a balance between buffer management and network scenarios without inducing excessive buffer under-runs or overflows. Compared to existing ABR schemes, our method reduces undesirable buffer events, including rebuffering events and buffer full events, while improving the average QoE by up to 71.88% and 75.25% over the HSDPA and FCC datasets, respectively.

A Hybrid Approach for WebRTC Video Streaming on Resource-Constrained Devices

This paper introduces thorough comparative and interoperability analyses involving a browser-based P2P video streaming approach utilizing WebRTC (web real-time communication), along with WebRTC hybrid solutions developed using the React Native framework (for mobile) and the electron framework (for desktop and Raspberry Pi). The assessment is carried out based on various metrics, including CPU (central processing unit) load, RAM (random access memory) utilization, and network data consumption. The obtained findings highlight the potential of integrating WebRTC P2P video streaming into hybrid applications as a promising alternative for real-time video streaming applications in embedded systems; given that, compared to the current mainstreams, e.g., Chrome or Firefox, the proposed approach has superiority performance in terms of CPU load, RAM usage, and network occupancy. The results also demonstrated that interoperability is ensured between the two types of applications (web-based and hybrid-based one).

An Apprenticeship Learning Approach for Adaptive Video Streaming Based on Chunk Quality and User Preference

Video traffic has experienced an exponential increase in current years due to the growing ubiquity of mobile equipment and the constant network improvement. To deliver video in high quality across various network conditions, adaptive bitrate (ABR) algorithms dynamically select bitrate for each chunk according to perceived network rate and buffer occupancy. Unfortunately, though ameliorating the quality of chunks with dynamic scenes can obtain more QoE gain than the ones with static scenes, current ABR algorithms generally aim to maximize the average bitrate rather than perceptual quality, resulting in the QoE degradation. To address this issue, we propose a dynamic-chunk quality-aware adaptive bitrate scheme via apprenticeship learning named DAVS (Dynamic-chunk quality Aware Video Streaming), in which higher quality is chosen for the dynamic chunks without decreasing the quality of static chunks excessively. Moreover, we take the user's viewing preference into account to make DAVS adapt to the QoE diversity. The experimental results show that DAVS enhances the quality of dynamic chunks and greatly improves the QoE compared with the state-of-the-art ABR algorithms.

QuDASH: Quantum-Inspired Rate Adaptation Approach for DASH Video Streaming

QuDASH: Quantum-Inspired Rate Adaptation Approach for DASH Video Streaming

KQI Assessment of VR Services: A Case Study on 360-Video Over 4G and 5G

Extended Reality (XR) arises as one of the current cutting-edge educational and entertainment emergent technologies. This service differs from traditional video streaming approaches due to its immersive experience, which allows the user to enjoy omnidirectional multimedia. However, the service experience must be guaranteed to avoid side effects such as cybersickness or disorientation. This work presents a framework to assess 360-video service streaming performance over mobile networks through Key Quality Indicators (KQIs) using VR (Virtual Reality) HMD (Head Mounted Device). The testbed is composed of a 360-video client for DASH (Dynamic Adaptive Streaming over HTTP), which playbacks multimedia content from a video server located in the cloud while KQI measuring tasks are performed in the user as on the network sides. Various metrics are collected such as resolution, frame rate, initial playback time, throughput, stall events, and round trip time (RTT), among various others. Finally, a performance comparison between LTE and 5G technologies is provided. Results from the KQI measurement highlight the potential of the new generation of mobile networks in the provision of service with high-quality levels of experience.

Super-Resolution-Empowered Adaptive Medical Video Streaming in Telemedicine Systems

Due to influence of COVID-19, telemedicine is becoming more and more important. High-quality medical videos can provide a physician with a better visual experience and increase the accuracy of disease diagnosis, but this requires a dramatic increase in bandwidth compared to that required by regular videos. Existing adaptive video-streaming approaches cannot successfully provide high-resolution video-streaming services under poor or fluctuating network conditions with limited bandwidth. In this paper, we propose a super-resolution-empowered adaptive medical video streaming in telemedicine system (named SR-Telemedicine) to provide high quality of experience (QoE) videos for the physician while saving the network bandwidth. In SR-Telemedicine, very low-resolution video chunks are first transmitted from the patient to an edge computing node near the physician. Then, a video super-resolution (VSR) model is employed at the edge to reconstruct the low-resolution video chunks into high-resolution ones with an appropriate high-resolution level (such as 720p or 1080p). Furthermore, the neural network of VSR model is designed to be scalable and can be determined dynamically. Based on the time-varying computational capability of the edge computing node and the network condition, a double deep Q-Network (DDQN)-based algorithm is proposed to jointly select the optimal reconstructed high-resolution level and the scale of the VSR model. Finally, extensive experiments based on real-world traces are carried out, and the experimental results illustrate that the proposed SR-Telemedicine system can improve the QoE of medical videos by 17–79% compared to three baseline algorithms.

QoCoVi: QoE- and cost-aware adaptive video streaming for the Internet of Vehicles

Recent advances in embedded systems and communication technologies enable novel, non-safety applications in Vehicular Ad Hoc Networks (VANETs). Video streaming has become a popular core service for such applications. In this paper, we present QoCoVi as a QoE- and cost-aware adaptive video streaming approach for the Internet of Vehicles (IoV) to deliver video segments requested by mobile users at specified qualities and deadlines. Considering a multitude of transmission data sources with different capacities and costs, the goal of QoCoVi is to serve the desired video qualities with minimum costs. By applying Dynamic Adaptive Streaming over HTTP (DASH) principles, QoCoVi considers cached video segments on vehicles equipped with storage capacity as the lowest-cost sources for serving requests. We design QoCoVi in two SDN-based operational modes: (i) centralized and (ii) distributed. In centralized mode, we can obtain a suitable solution by introducing a mixed-integer linear programming (MILP) optimization model that can be executed on the SDN controller. However, to cope with the computational overhead of the centralized approach in real IoV scenarios, we propose a fully distributed version of QoCoVi based on the proximal Jacobi alternating direction method of multipliers (ProxJ-ADMM) technique. The effectiveness of the proposed approach is confirmed through emulation with Mininet-WiFi in different scenarios.

Remote Driving Control With Real-Time Video Streaming Over Wireless Networks: Design and Evaluation

There is currently a large gap between current AI based autonomous-driving cars and fully autonomous cars. In such situation, remote control of cars can be a unique solution to fill the gap. Human assistance through remote driving can provide more flexibility and intelligence than a single artificial intelligence. Furthermore, remote driving enables valuable operational data to be obtained, thus laying the groundwork for gradually providing autonomous driving performance in the future. Remote driving of a vehicle is to drive a vehicle remotely via communication infrastructure. Latency in communication technology has a significant impact on the real-time control of the vehicles as well as responding to unexpected situations. For remotely driven vehicles, real-time transmission of data and images is particularly important. The latency or delay between a vehicle and the controller depends on the communication methods and transport protocols of the video streaming. Furthermore, the control or driving performance also depends on the speed of the vehicle. Therefore, in this paper, we explore the impact of different communication methods of video streaming and vehicle “speed” on the performance of remote driving.We design a vehicle remote driving system based on ROS (Robot Operating System) with ROS as the core communication architecture to realize remote control of vehicles. The video stream is transmitted using three different streaming methods such as ROS multi-computer communication, TCP protocol, and UDP protocol. To be specific, we implement a simple remote driving system for a “model” car and let multiple people drive the car through the remote driving system at different speeds. Then, we analyze how the drivers perform in terms of whether the vehicle gets off the track while driving.We quantify the performance of remote driving at different speeds and with different protocols of video transmission methods and perform comparative analysis. We find that “UDP protocol based video streaming” to achieve 720P video streaming with a latency of less than 50ms, which is helpful for further research on remote driving. Furthermore, to evaluate the impact of the video streaming approach on remote driving, we conduct some experiments by “directly” observing the vehicle with eyes instead of using video streaming. In this way, we remove the latency of video streaming. The experiment results show that the speed of the vehicle and the video streaming methods have a significant impact on the driving performance and demonstrate that the UDP protocol-based video streaming method is better suited for remote driving. The results imply that remote driving should be used in a low-speed environment rather than at high speed.

OSCAR: On Optimizing Resource Utilization in Live Video Streaming

Live video streaming traffic and related applications have experienced significant growth in recent years. However, this has been accompanied by some challenging issues, especially in terms of resource utilization. Although IP multicasting can be recognized as an efficient mechanism to cope with these challenges, it suffers from many problems. Applying software-defined networking (SDN) and network function virtualization (NFV) technologies enable researchers to cope with IP multicasting issues in novel ways. In this article, by leveraging the SDN concept, we introduce OSCAR (Optimizing reSourCe utilizAtion in live video stReaming) as a new cost-aware video streaming approach to provide advanced video coding (AVC)-based live streaming services in the network. In this article, we use two types of virtualized network functions (VNFs): virtual reverse proxy (VRP) and virtual transcoder function (VTF). At the edge of the network, VRPs are responsible for collecting clients’ requests and sending them to an SDN controller. Then, by executing a mixed-integer linear program (MILP), the SDN controller determines a group of optimal multicast trees for streaming the requested videos from an appropriate origin server to the VRPs. Moreover, to elevate the efficiency of resource allocation and meet the given end-to-end latency threshold, OSCAR delivers only the highest requested quality from the origin server to an optimal group of VTFs over a multicast tree. The selected VTFs then transcode the received video segments and transmit them to the requesting VRPs in a multicast fashion. To mitigate the time complexity of the proposed MILP model, we present a simple and efficient heuristic algorithm that determines a near-optimal solution in polynomial time. Using the MiniNet emulator, we evaluate the performance of OSCAR in various scenarios. The results show that OSCAR surpasses other SVC- and AVC-based multicast and unicast approaches in terms of cost and resource utilization.

A Stochastic-Based Rate Control Approach for Video Streaming Over Cognitive Radio Networks

Recently, the problem of video streaming over cognitive radio networks has attracted the attention of the research community due to the multiple challenges facing it. These challenges stem from the nature of the video content itself, the stochastic nature of wireless links as well as the intermittent availability of the licensed primary channels. Motivated by the above, this paper follows a different approach to tackle such a problem. Specifically, the proposed approach relies on obtaining an exact closed-form expression for the statistical distribution of the total amount of data that can be transmitted over the available primary users’ channels for any arbitrary interval of time. The validity of the obtained expression is verified through a comparison with Monte Carlo simulations. We then use this expression to propose a simple low complexity stochastic-based rate control approach for video streaming over cognitive radio networks. Due to its low computational complexity, the proposed approach can be easily implemented by any base station. The performance of the proposed approach is evaluated using the SimEvents simulator through reporting the average buffer occupancy, the peak signal-to-noise ratio and the inter-starvation interval. These metrics were reported for different parameters settings.

Rate-Utility Optimized Streaming of Volumetric Media for Augmented Reality

Volumetric media, popularly known as holograms, need to be delivered to users using both on-demand and live streaming, for new augmented reality and virtual reality experiences. As in video streaming, hologram streaming must support network adaptivity and fast startup, but must also handle large bandwidths, multiple simultaneously streaming objects, and frequent user interaction, which requires a low delay. In this paper, we introduce the first system to our knowledge designed specifically for streaming volumetric media. The system reduces bandwidth by introducing 3D tiles and culling them or reducing their level of detail depending on their relation to the user’s view frustum and distance to the user. Our system reduces latency by introducing a window-based buffer, which in contrast to a queue-based buffer allows insertions near the head of the buffer rather than only at the tail of the buffer, to respond quickly to user interaction. To allocate bits between different tiles across multiple objects, we introduce a simple greedy yet provably optimal algorithm for rate-utility optimization. We introduce utility measures based not only on the underlying quality of the representation, but on the level of detail relative to the user’s viewpoint and device resolution. The simulation results show that the proposed algorithm provides a superior quality compared with the existing video-streaming approaches adapted to hologram streaming, in terms of utility and user experience over variable, throughput-constrained networks.

A cloud-fog scalable video streaming architecture for multimedia internet of things devices

Cloud computing can be used to provide guaranteed quality of video services. On the other hand, fog computing can lower the transmission costs by using collaborative machine-to-machine and near-user edge communications. In this paper, a cloud-fog scalable video streaming architecture for multimedia internet of things (IoT) devices is proposed, where fog nodes can collaborate with the rented cloud servers for efficiently delivering the requiring video frames to multimedia IoT devices. The proposed architecture strengthens the stability of the video streaming transmission and improves the quality of service (QoS) of multimedia IoT services with cloud-fog computing techniques. Simulation results show that the proposed cloud-fog scalable video streaming architecture can provide smooth video streaming services to multimedia IoT devices with low-cost than traditional video streaming approaches.

Delay–Rate–Distortion Optimization for Cloud Gaming With Hybrid Streaming

Cloud gaming as the emerging game service has attracted significant attention. However, traditional video streaming approach suffers from high bandwidth consumption, and traditional graphics streaming approach requires a long initial period to download game models. In this paper, we propose a novel hybrid streaming framework, jointly applying video streaming and graphics streaming to provide a high-quality gaming experience. In the proposed framework, cloud servers not only transmit the encoded video frames but also progressively transmit the graphics data, which are used to render a game frame to provide an additional reference to the video encoder. Based on the proposed framework, we investigate the delay–rate–distortion optimization problem, where the source rate between the video stream and the graphics stream is optimized to minimize the overall distortion under the bandwidth and response delay constraints. The experimental results demonstrate that the proposed hybrid streaming can achieve the lowest distortion under the constraints of bandwidth and response delay, compared with the traditional video streaming and graphics streaming.

Categorization-based video streaming for traffic mitigation in content delivery services

This paper presents a categorization-based video streaming approach (CVS) to mitigate the Internet traffic in content delivery services. The CVS uses the statistical information related to the users’ view patterns (i.e., the average view duration in accordance with the playback time of the content) to adjust the request period of content chunks. Therefore, it can reduce the amount of Internet traffic by reducing unnecessary chunk requests and content chunks. The operation of the CVS is based on the average view duration provided by the content provider (CP). However, even if the CP does not provide the average view duration of the content, the CVS can properly predict the average view duration by using the content categorization and adjust the request period of content chunks. The simulation results show that the CVS achieves better performance in terms of the average waste ratio of network resources, the amount of network traffic, and the number of chunk requests.

Secure Channel Allocation Approach for Video Streaming in Cognitive Radio Networks

In this paper, we propose a technique to ensure security in cooperative spectrum sensing for video streaming application in cognitive radio network. Sensing based clustering algorithm puts together the cognitive radios into clusters to mitigate cooperative overhead and to reduce energy consumption. In order to avert primary user emulation attacks, we introduce authentication signature ID algorithm which provides authority for the licensed user to use the spectrum. To diminish spectrum sensing data falsification attacks, we exploit hamming algorithm to expel forgery node from spectrum sensing decision. Furthermore, the primary radio network channel allocation algorithm is employed to precisely deliver the video with high quality and lower delay. For video transmission in a cognitive radio network, we compress the video frames using block truncation coding-pattern fitting in order to reduce bandwidth requirement. This compression technique offers good video transmission with a high compression ratio in a cognitive radio network, due to trade-off among quality, bit rate and decoding time. Simulation results reveal that there is a significant improvement in the detection rate of idle channel and reduction of delay for high-quality video transmission.

LiveRender: A Cloud Gaming System Based on Compressed Graphics Streaming

In cloud gaming systems, the game program runs at servers in the cloud, while clients access game services by sending input events to the servers and receiving game scenes via video streaming. In this paradigm, servers are responsible for all performance-intensive operations, and thus suffer from poor scalability. An alternative paradigm is called graphics streaming, in which graphics commands and data are offloaded to the clients for local rendering, thereby mitigating the server’s burden and allowing more concurrent game sessions. Unfortunately, this approach is bandwidth-consuming, due to large amounts of graphic commands and geometry data. In this paper, we present LiveRender, an open-source gaming system that remedies the problem by implementing a suite of bandwidth optimization techniques including intraframe compression, interframe compression, and caching, establishing what we call compressed graphics streaming. Experiments results show that the new approach is able to reduce bandwidth consumption by 52%–73% compared to raw graphics streaming, with no perceptible difference in video quality and reduced response delay. Compared to the video streaming approach, LiveRender achieves a traffic reduction of 40%–90% with even improved video quality and substantially smaller response delay, while enabling higher concurrency at the server.

A QoE centric distributed caching approach for vehicular video streaming in cellular networks

AbstractDistributed caching‐empowered wireless networks can greatly improve the efficiency of data storage and transmission and thereby the users' quality of experience (QoE). However, how this technology can alleviate the network access pressure while ensuring the consistency of content delivery is still an open question, especially in the case where the users are in fast motion. Therefore, in this paper, we investigate the caching issue emerging from a forthcoming scenario where vehicular video streaming is performed under cellular networks. Specifically, a QoE centric distributed caching approach is proposed to fulfill as many users' requests as possible, considering the limited caching space of base stations and basic user experience guarantee. Firstly, a QoE evaluation model is established using verified empirical data. Also, the mathematic relationship between the streaming bit rate and actual storage space is developed. Then, the distributed caching management for vehicular video streaming is formulated as a constrained optimization problem and solved with the generalized–reduced gradient method. Simulation results indicate that our approach can improve the users' satisfaction ratio by up to 40%. Copyright © 2015 John Wiley & Sons, Ltd.

A Control-Theoretic Approach for Dynamic Adaptive Video Streaming over HTTP

User-perceived quality-of-experience (QoE) is critical in Internet video applications as it impacts revenues for content providers and delivery systems. Given that there is little support in the network for optimizing such measures, bottlenecks could occur anywhere in the delivery system. Consequently, a robust bitrate adaptation algorithm in client-side players is critical to ensure good user experience. Previous studies have shown key limitations of state-of-art commercial solutions and proposed a range of heuristic fixes. Despite the emergence of several proposals, there is still a distinct lack of consensus on: (1) How best to design this client-side bitrate adaptation logic (e.g., use rate estimates vs. buffer occupancy); (2) How well specific classes of approaches will perform under diverse operating regimes (e.g., high throughput variability); or (3) How do they actually balance different QoE objectives (e.g., startup delay vs. rebuffering). To this end, this paper makes three key technical contributions. First, to bring some rigor to this space, we develop a principled control-theoretic model to reason about a broad spectrum of strategies. Second, we propose a novel model predictive control algorithm that can optimally combine throughput and buffer occupancy information to outperform traditional approaches. Third, we present a practical implementation in a reference video player to validate our approach using realistic trace-driven emulations.