Adaptive Video Streaming Research Articles

Learning Accurate Network Dynamics for Enhanced Adaptive Video Streaming

Learning Accurate Network Dynamics for Enhanced Adaptive Video Streaming

Learned fractional downsampling network for adaptive video streaming

Given increasing demand for very large format contents and displays, spatial resolution changes have become an important part of video streaming. In particular, video downscaling is a key ingredient that streaming providers implement in their encoding pipeline as part of video quality optimization workflows. Here, we propose a downsampling network architecture that progressively reconstructs residuals at different scales. Since the layers of convolutional neural networks (CNNs) can only be used to alter the resolutions of their inputs by integer scale factors, we seek new ways to achieve fractional scaling, which is crucial in many video processing applications. More concretely, we utilize an alternative building block, formulated as a conventional convolutional layer followed by a differentiable resizer. To validate the efficacy of our proposed downsampling network, we integrated it into a modern video encoding system for adaptive streaming. We extensively evaluated our method using a variety of different video codecs and upsampling algorithms to show its generality. The experimental results show that improvements in coding efficiency over the conventional Lanczos algorithm and state-of-the-art methods are attained, in terms of PSNR, SSIM, and VMAF, when tested on high-resolution test videos. In addition to quantitative experiments, we also carried out a subjective quality study, validating that the proposed downsampling model yields favorable results.

Adaptive Processing of Camera Video Stream with Limitations on the Network Data Transmission Bandwidth

Video surveillance systems, cameras, and video stream processing are actively used in many enterprises as a monitoring and control tool for regular and emergency situations, as well as staff activities. The application of intelligent algorithms allows tracking and minimizing operator errors, but these approaches are highly sensitive to the quality of the original video, presence of noise, and low resolution. On the other hand, such video surveillance systems may be limited by network bandwidth. Therefore, this work considers an adaptive video stream processing algorithm that ensures efficient operation of computer vision and object recognition methods while minimizing the amount of transmitted information within network bandwidth constraints. The proposed algorithm addresses the task of determining boundary conditions that ensure the functionality of object recognition algorithms with the least amount of video stream. Corresponding experimental studies were conducted to determine the minimum values of frame resolution and video bitrate. The algorithm was tested in organizing video surveillance at warehouse complexes. The obtained results can be used in developing decision support systems for enterprises in various industries requiring intelligent processing of large volumes of data.

Blockchain-Driven Assurance: Transforming Adaptive Video Streaming with TamperResistant Quality of Service Metrics

As the demand for high-quality video streaming experiences continues to rise, ensuring reliable and accountable Quality of Service (QoS) metrics becomes paramount. This review paper explores the transformative potential of blockchain technology in addressing the challenges associated with adaptive video streaming. By establishing a decentralized and tamper-resistant ledger, blockchain contributes to transparent QoS metrics, mitigating existing limitations in reliability and accountability. The conceptual framework involves the integration of blockchain principles, particularly smart contracts, to automate and enforce service level agreements. The paper delves into the advantages of blockchain, such as transparency, security, and tamper resistance, while addressing scalability issues and adoption challenges. Through an extensive literature review, case studies, and discussions on future directions, this paper provides insights into the current state, potential improvements, and emerging trends in leveraging blockchain for enhancing the quality of adaptive video streaming services.

AP-assisted adaptive video streaming in wireless networks with high-density clients

Adaptive video streaming over wireless networks has experienced tremendous growth in past few years. In order to guarantee users’ quality of experience (QoE), adaptive bitrate (ABR) algorithms have been extensively studied. With the recent emergency of high-density Wi-Fi networks, these solutions no longer perform well. On the one hand, bitrate decisions respond slowly to high network fluctuations, and on the other hand, wireless channel resources are insufficient especially under the cases that multiple clients compete for the limited bandwidth. Hence, users’ personalized QoE metrics need to be taken into account to cope with the QoE degradation. To this end, we design an access point (AP) assisted wireless dynamic adaptive video streaming over Hypertext Transfer Protocol (HTTP) solution called Wi-DASH, which aims to improve users’ QoE while considering channel utilization. In Wi-DASH, the video server aggregates real-time network status with the assistance of AP, and estimates clients’ player status through statistical analysis of chunk request logs. On this basis, a deep reinforcement learning (DRL) based ABR algorithm is designed, where the DRL model can deal with the complicated global status information including network status, player status, and QoE preferences. Finally, we implement Wi-DASH system, and conduct experiments with 4K resolution videos. Experimental results reveal that the Wi-DASH can more fully utilize wireless channel resources, and significantly improve users’ QoE.

Joint Optimization of QoE and Fairness for Adaptive Video Streaming in Heterogeneous Mobile Environments

The rapid growth of mobile video traffic and user demand poses a more stringent requirement for efficient bandwidth allocation in mobile networks where multiple users may share a bottleneck link. This provides content providers an opportunity to jointly optimize multiple users’ experiences but users often suffer short connection durations and frequent handoffs because of their high mobility. In this paper, we propose an end-to-end scheme, VSiM, for supporting mobile video streaming applications in heterogeneous wireless networks. The key idea is allocating bottleneck bandwidth among multiple users based on their mobility profiles and Quality of Experience (QoE)-related knowledge to achieve max-min QoE fairness. Besides, the QoE of buffer-sensitive clients is further improved by the novel server push strategy based on HTTP/3 protocol without affecting the existing bandwidth allocation approach or sacrificing other clients’ view quality. VSiM is lightweight and easy to deploy in the real world without touching the underlying network infrastructure. We evaluated VSiM experimentally in both simulations and a lab testbed on top of the HTTP/3 protocol. We find that the clients’ QoE fairness of VSiM achieves more than 40% improvement compared with state-of-the-art solutions, i.e., the viewing quality of clients in VSiM can be improved from 720p to 1080p in resolution. Meanwhile, VSiM provides about 20% improvement of average QoE.

Convex Hull Prediction for Adaptive Video Streaming by Recurrent Learning.

Adaptive video streaming relies on the construction of efficient bitrate ladders to deliver the best possible visual quality to viewers under bandwidth constraints. The traditional method of content dependent bitrate ladder selection requires a video shot to be pre-encoded with multiple encoding parameters to find the optimal operating points given by the convex hull of the resulting rate-quality curves. However, this pre-encoding step is equivalent to an exhaustive search process over the space of possible encoding parameters, which causes significant overhead in terms of both computation and time expenditure. To reduce this overhead, we propose a deep learning based method of content aware convex hull prediction. We employ a recurrent convolutional network (RCN) to implicitly analyze the spatiotemporal complexity of video shots in order to predict their convex hulls. A two-step transfer learning scheme is adopted to train our proposed RCN-Hull model, which ensures sufficient content diversity to analyze scene complexity, while also making it possible to capture the scene statistics of pristine source videos. Our experimental results reveal that our proposed model yields better approximations of the optimal convex hulls, and offers competitive time savings as compared to existing approaches. On average, the pre-encoding time was reduced by 53.8% by our method, while the average Bjøntegaard delta bitrate (BD-rate) of the predicted convex hulls against ground truth was 0.26%, and the mean absolute deviation of the BD-rate distribution was 0.57%.

SOHO-FL: A Fast Reconvergent Intra-Domain Routing Scheme Using Federated Learning

The development of machine learning provides a new paradigm for network optimizations, e.g., reinforcement learning (RL) has brought great improvements in many fields, such as adaptive video streaming, congestion control of TCP. The fundamental mechanism of such RL-based architectures is that the neural network decision model converges to a stable state by continuously interacting with network environment. However, for network routing problem, such RL-based strategies do not work well due to topology change. This is because topological changes would require the existing RL models to be retrained, while these models may stop making routing decisions or provide non-optimal decisions during the slow reconverging process of retraining, seriously affected transmission performance. To solve this problem, we proposed a fast convergent RL-model (SOHOFL), which can alleviate the performance degradation caused by the slow retraining process by federated learning. The experimental results based on real-world network topologies demonstrate that SOHO-FL outperforms the state-of-the-art algorithms in reconvergence time by 22.3% on average.

Adaptive Video Streaming in Integrated Satellite-Terrestrial Networks: A Low-Complexity Model Predictive Control Approach

Adaptive Video Streaming in Integrated Satellite-Terrestrial Networks: A Low-Complexity Model Predictive Control Approach

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

MMVS: Enabling Robust Adaptive Video Streaming for Wildly Fluctuating and Heterogeneous Networks

MMVS: Enabling Robust Adaptive Video Streaming for Wildly Fluctuating and Heterogeneous Networks

A Survey on QoE Management Schemes for HTTP Adaptive Video Streaming: Challenges, Solutions, and Opportunities

A Survey on QoE Management Schemes for HTTP Adaptive Video Streaming: Challenges, Solutions, and Opportunities

User-Centric Algorithms: Sculpting the Future of Adaptive Video Streaming

This paper explores the transformative potential of user-centric algorithms in shaping the future landscape of adaptive video streaming. Traditional streaming methods, though effective, often lack the ability to dynamically adapt to individual user preferences. We argue for a paradigm shift towards user-centricity, where algorithms are designed to consider user behavior, preferences, and feedback to optimize content delivery. By examining the impact of personalized streaming experiences on viewer satisfaction and engagement, we present a compelling case for the integration of machine learning and artificial intelligence in adaptive video streaming. Through real-world examples and case studies, we showcase the efficacy of user-centric algorithms in sculpting a more tailored and immersive streaming environment. This paper provides insights into the challenges, considerations, and future trends surrounding user-centric adaptive video streaming, highlighting its potential to redefine the viewer experience and set new standards for digital content delivery.

The Protocol Play: DASHing Ahead or HLSing Back?

This paper presents a comprehensive comparative analysis of two widely adopted adaptive streaming protocols, Dynamic Adaptive Streaming over HTTP (DASH) and HTTP Live Streaming (HLS), within the contemporary landscape of video delivery. Delving into the background of both protocols, the study explores their architectures, media format support, and adaptability to diverse network conditions. A meticulous examination of strengths and weaknesses encompasses performance, scalability, user experience, device compatibility, and implementation ease. The implications for content delivery, including considerations of quality of experience, Content Delivery Network (CDN) compatibility, and cost factors, are thoroughly discussed. The paper also sheds light on current trends, emerging technologies, and the evolving nature of DASH and HLS. In conclusion, the study provides a synthesis of key insights, offering practical recommendations for protocol selection based on specific use cases and contributing valuable perspectives to the dynamic realm of adaptive video streaming.

Adaptive video streaming solution based on multi-access edge computing advantages

Adaptive video streaming solution based on multi-access edge computing advantages

Plausibility of BBR as CUBIC’S Replacement and proposed improvement to BBR using GENET

Recent advancements in deep reinforcement learning (RL) have opened new avenues for enhancing network congestion control algorithms. Our research builds upon these developments, particularly focusing on the BBR (Bottleneck Bandwidth and Round-trip propagation time) congestion control algorithm. We propose integrating GENET's reinforcement learning framework, a novel training paradigm that has demonstrated success in various network adaptation algorithms, including adaptive video streaming, congestion control, and load balancing. GENET leverages curriculum learning to effectively train RL models by progressively introducing more challenging network environments. This method counters the common pitfalls in RL training, such as suboptimal performance in a wide range of environments and poor generalization in narrowly defined training scenarios. Our approach exploits the strengths of GENET in identifying and emphasizing network conditions where the current RL model underperforms compared to traditional rule-based baselines, thereby facilitating significant improvements. This research aims to demonstrate that applying GENET's methodology to the BBR congestion control algorithm can yield RL policies that surpass both regularly trained RL policies and conventional baselines, thereby advancing the efficiency and reliability of network congestion control.

A Visual Sensitivity Aware ABR Algorithm for DASH via Deep Reinforcement Learning

In order to cope with the fluctuation of network bandwidth and provide smooth video services, adaptive video streaming technology is proposed. In particular, the adaptive bitrate (ABR) algorithm is widely used in dynamic adaptive streaming over HTTP (DASH) to improve quality of experience (QoE). However, existing ABR algorithms still ignore the inherent visual sensitivity of human visual system (HVS). As the final receiver of video, HVS has different sensitivity to the quality distortion of different video content, and video content with high visual sensitivity needs to allocate more bitrate resources. Therefore, existing ABR algorithms still have limitations in reasonably allocating bitrate and maximizing QoE. To solve this problem, this paper designs an adaptive bitrate strategy from the perspective of user vision, studies the modeling of visual sensitivity, and proposes a visual sensitivity aware ABR algorithm. We extract a set of content features and attribute features from the video, and consider the simulation of HVS to establish a total masking effect model that reflects the visual sensitivity more accurately. Further, the network status, buffer occupancy, and visual sensitivity are comprehensively considered under a deep reinforcement learning framework to select the appropriate bitrate for maximizing QoE. We implement the proposed algorithm over a realistic trace-driven evaluation and compare its performance with several latest algorithms. Experimental results show that our algorithm can align ABR strategy with visual sensitivity to achieve better QoE in high visual sensitivity content, and improves the average perceptual video quality and overall user QoE by 18.3% and 22.8%, respectively. Additionally, we prove the feasibility of our algorithm through subjective evaluation in the real environment.

Churn-tolerant CDN at the edge for adaptive video streaming: towards multi-connection approach using HTTP/2

Churn-tolerant CDN at the edge for adaptive video streaming: towards multi-connection approach using HTTP/2

Adaptive Video Streaming With Automatic Quality-of-Experience Optimization

Video streaming has grown tremendously in recent years and it is now one of the main applications on the Internet. Due to the networks' inherent bandwidth fluctuations, various rate-adaptive streaming algorithms have been developed to compensate for such fluctuations to improve Quality-of-Experience (QoE). However, in practice, the preference for QoE typically differs significantly across different viewers and there is no systematic way so far to comprehensively incorporate different sets of conflicting QoE objectives into the algorithm design. Thus, it is not surprising that the QoE performance achieved by the existing algorithms is in fact far from optimal. This work aims at attacking the heart of the problem by developing a novel framework called Post Streaming Quality Analysis (PSQA) that can maximize the QoE under any preference through automatically tuning the adaptation logic of the streaming algorithms. Evaluation results show that the QoE achieved by PSQA is substantially better than the existing approaches and in some scenarios even close to optimal. Moreover, PSQA can be readily implemented into real streaming platforms, offering a practical and reliable solution for high-performance streaming services.

Power efficient UAV placement and resource allocation for adaptive video streaming in wireless networks

Unmanned aerial vehicle (UAV) enabled wireless communication that can transmit/receive data to/from ground users, especially at cell boundaries or in disaster-stricken areas where the ground-based infrastructure has been damaged, is an emerging area of research. These aerial vehicles require energy for communication and are typically energy constrained. The communication system becomes more energy hungry when it comes to video streaming of high quality. Because for higher video quality streaming, higher bitrate and more bandwidth is required. Higher transmission power and rate will result in increased streaming quality. We concentrate on UAV-aided power efficient adaptive video streaming wireless networks with multiple ground users, taking into account the available energy budget and information causality constraints. The objective of this research work is to maximize adaptive video streaming utility per unit of power in a UAV-enabled wireless communication network by optimizing the position of UAV and allocation of communication resources, such as bandwidth, and transmit power, while competing with system-designed constraints, such as energy budget, transmission rate, and allocated bandwidth. The problem that is addressed is non-convex which is approached by applying successive convex approximation, Dinkelbach method and coordinates decent techniques, and an efficient iterative algorithm is devised for its solution. Extensive simulations show that the suggested optimization strategy outperforms the results of the previously proposed works.