Performance Of Video Streaming Research Articles

Multi-agent DRL-based Multipath Scheduling for Video Streaming with QUIC

The popularization of video streaming brings challenges in satisfying diverse Quality of Service (QoS) requirements. The multipath extension of the Quick UDP Internet Connection (QUIC) protocol, also called MPQUIC, has the potential to improve video streaming performance with multiple simultaneously transmitting paths. The multipath scheduler of MPQUIC determines how to distribute the packets onto different paths. However, while applying current multipath schedulers into MPQUIC, our experimental results show that they fail to adapt to various receive buffer sizes of different devices and comprehensive QoS requirements of video streaming. These problems are especially severe under heterogeneous and dynamic network environments. To tackle these problems, we propose MARS, a Multi-agent deep Reinforcement learning (MADRL)-based Multipath QUIC Scheduler, which is able to promptly adapt to dynamic network environments. It exploits the MADRL method to learn a neural network for each path and generate scheduling policy. Besides, it introduces a novel multi-objective reward function that takes out-of-order queue size and different QoS metrics into consideration to realize adaptive scheduling optimization. We implement MARS in an MPQUIC prototype and deploy in Dynamic Adaptive Streaming over HTTP system. Then, we compare it with the state-of-the-art multipath schedulers in both emulated and real-world networks. Experimental results show that MARS outperforms the other schedulers with better adaptive capability regarding the receive buffer sizes and QoS.

Cloud‐based video streaming services: Trends, challenges, and opportunities

AbstractCloud computing has drastically changed the delivery and consumption of live streaming content. The designs, challenges, and possible uses of cloud computing for live streaming are studied. A comprehensive overview of the technical and business issues surrounding cloud‐based live streaming is provided, including the benefits of cloud computing, the various live streaming architectures, and the challenges that live streaming service providers face in delivering high‐quality, real‐time services. The different techniques used to improve the performance of video streaming, such as adaptive bit‐rate streaming, multicast distribution, and edge computing are discussed and the necessity of low‐latency and high‐quality video transmission in cloud‐based live streaming is underlined. Issues such as improving user experience and live streaming service performance using cutting‐edge technology, like artificial intelligence and machine learning are discussed. In addition, the legal and regulatory implications of cloud‐based live streaming, including issues with network neutrality, data privacy, and content moderation are addressed. The future of cloud computing for live streaming is examined in the section that follows, and it looks at the most likely new developments in terms of trends and technology. For technology vendors, live streaming service providers, and regulators, the findings have major policy‐relevant implications. Suggestions on how stakeholders should address these concerns and take advantage of the potential presented by this rapidly evolving sector, as well as insights into the key challenges and opportunities associated with cloud‐based live streaming are provided.

User Dynamics-Aware Edge Caching and Computing for Mobile Virtual Reality

In this paper, we present a novel content caching and delivery approach for mobile virtual reality (VR) video streaming. The proposed approach aims to maximize VR video streaming performance, i.e., minimizing video frame missing rate, by proactively caching popular VR video chunks and adaptively scheduling computing resources at an edge server based on user and network dynamics. First, we design a scalable content placement scheme for deciding which video chunks to cache at the edge server based on tradeoffs between computing and caching resource consumption. Second, we propose a machine learning-assisted VR video delivery scheme, which allocates computing resources at the edge server to satisfy video delivery requests from multiple VR headsets. A Whittle index-based method is adopted to reduce the video frame missing rate by identifying network and user dynamics with low signaling overhead. Simulation results demonstrate that the proposed approach can significantly improve VR video streaming performance over conventional caching and computing resource scheduling strategies.

Interoperabilitas NDN-DPDK dan Forwarding NFD dalam Video Streaming berbasis NDN pada Virtual Machine

The Named Data Network (NDN) is a future network concept that addresses some of the issues present in the current internet architecture. One of the main advantages of NDN is the use of content caching at each router, enabling faster and more efficient data access. As internet network technology develops, the demand for video content continues to increase every year. Therefore, video optimization and delivery require high access speeds so that clients can run content from the server seamlessly. This research incorporates NDN-DPDK (Named Data Network-DData Plane Development Kit), which is designed as hardware acceleration, and uses NDN's default forwarder, Network Forwarding Daemon (NFD), which allows a wide variety of experiments with NDN architecture. The implementation and analysis of video streaming performance will use Quality of Service (QoS) parameters such as RTT, throughput, and startup delay to measure the quality of video streaming on the network side. In addition, CPU measurements are made on the router closest to the client to evaluate the traffic load required by the forwarder to transmit data. The results of implementation and analysis in this study obtained video streaming quality on the second accessor with an RTT time of 4 seconds, throughput above 0.4 MBps, a video startup delay of 8 seconds, and 100% CPU usage.

A Taxonomy for Deep Learning in Dynamic Adaptive Video Streaming Over HTTP

Deep Learning (DL) has become a fundamental technology in the field of Dynamic Adaptive Video Streaming over HTTP (DASH), enabling significant advancements in video streaming systems. This taxonomy presents a novel framework for categorizing and organizing the diverse applications and methodologies of DL in DASH. The taxonomy encompasses various aspects of DL, including video representation, quality of experience (QoE) estimation, bitrate adaptation, buffer management, content- and context-aware adaptation, and network optimization. By providing a comprehensive overview of DL in DASH, this taxonomy serves as a valuable resource for researchers and practitioners, facilitating a better understanding of the different DL techniques and their applications in enhancing video streaming performance and user experience.

Rain conditions effect the transmission of Streaming video data on Aeromodelling

Streaming is the process of sending data continuously or continuously that be broadcast over the internet. FPV (First-person view) is a method used to control radio control vehicles from the pilot. Analysis of live video streaming service on FPV aeromodelling with standard configurations to determine the maximum results for live video streaming service on FPV aeromodelling. Distance measurements and environmental conditions are also necessary to determine the performance of live video streaming. Then performed a Quality of Service (QoS) analysis, including measurement of delay, jitter, and throughput using Wireshark. From the tests that have been carried out, the comparison of the best value between the measurement of data delay with a value of 0.0085 m/s, for a jitter of 20.294 m/s and a throughput of 0.009 m/s is obtained, all of which are in accordance with the standards recommended by ITU-T, so that the overall QoS obtained gives sufficient results

Buffer-Aware Virtual Reality Video Streaming With Personalized and Private Viewport Prediction

Viewport prediction and prefetch have an important influence on VR video streaming performance. This work proposes a novel federated learning-based viewport prediction model training algorithm, ComPer-FedAvg. The proposed algorithm leverages a VR video’s common viewing pattern and users’ personal viewing patterns to train the prediction model in a distributed and privacy-preserving manner. Further, considering the VR video viewport prediction accuracy, a stochastic game is formulated to solve the VR streaming network’s communication resource allocation problem, where limited communication resource blocks are auctioned to users to achieve the optimal overall VR viewing experience. For each user, the auction is decomposed into two disjoint subproblems, namely, the optimal number of data rate requesting and true value claiming (bidding). The optimal true value claiming has been analytically proved to be equal to the VR viewing reward with given data rate. Due to the lack of global information when users request data rate, we reformulate users’ data rate requesting problem as a POMDP problem. A novel deep reinforcement learning algorithm is adopted to solve the problem. Evaluation and simulation results show the proposed viewport prediction and VR streaming schemes outperform conventional solutions in terms of prediction accuracy and VR viewing experience.

Analisis Perbandingan Performa Codec H.264 & H.265 Video Streaming Dari Segi Quality of Service

Video streaming is a technology that is often used when watching videos on the internet without having to download then video to play it. Some problems that affect the performance of video streaming are such as the large capacity of the video size, the capacity of the video size affects the smoothness when streaming video. In this research, two video compression methods will be compared, namely H.264 codec and H.265 codec. The test is carried out to determine the effect of using the compression method according to changes in the codec ang framerate used. After testing the two codec, it can be concluded that the H.265 codec is more effective to apply than the H.264 codec.

Multi-Tier Caching and Resource Sharing for Video Streaming in 5G Ultra-Dense Networks

In this letter, we formulate a multi-tier caching and resource sharing (CRS) optimization problem for a high video streaming performance in 5G ultra-dense networks. Particularly, in user tier, the optimal sets of spectrum owning users (SUs) with available downlink resources, caching users (CUs) with cached video versions, and normal users (NUs) requesting the video versions are tripartite for video streaming over device-to-device (D2D) communications. In femto tier, the optimal caching strategy is found to indicate which video versions and where to cache them in the femtocell base station (FBSs). By cooperating with the video versions cached in macro tier, i.e., macro base station (MBS), the mobile users (MUs) can receive the video versions from D2D communications, FBSs, and MBS flexibly. The objective of the CRS solution is to maximize the video playback quality, while saving the caching storage of FBSs and satisfying a given throughput required by MUs and a given target signal to interference plus noise ratio of SUs.

ERRANT: Realistic emulation of radio access networks

Mobile devices drastically changed how people use the Internet. We use smartphones to access a heterogeneous catalog of web services such as news, social networks, audio/video streaming. Differently from wired connections, mobile networks do not offer the same kind of performance stability yet. Thus, service providers have to handle different network scenarios, e.g., 3G or 4G, while promising good end-users’ quality of experience (QoE). To ensure that QoE is adequate, it is necessary to thoroughly test applications with a wide range of possible network conditions. For this, network emulation is of vital importance as it allows a tester to run experiments with a wide range of network conditions. However, when it comes to mobile networks, the variety of technical characteristics, coupled with the opaque network configurations, makes realistic emulation a challenging task. Most of the freely available emulation tools rely on a simple emulation, offering limited variability performances for each network condition.In this paper, we propose ERRANT, EmulatoR of Radio Access NeTworks, an open-source tool that emulates mobile networks with a high level of realism, following a data-driven approach. We use a large-scale dataset composed of 100 k speed test measurements collected from 4 network operators in 2 countries. We create 32 different network profiles based on different countries, operators, radio access technologies, and signal qualities. For each profile, we obtain both typical behavior and variability for latency, download and upload bandwidth. We use the profiles to create models by means of the Kernel Density Estimation. Then, ERRANT employs the tc-netem Linux tool and the models for emulation. In this way, ERRANT offers realistic network emulation, in which both typical behavior and network variability are accurately recreated.We validate ERRANT models with an independent dataset of HTTP downloads performed on the same mobile networks as of the profiles. Results show the effectiveness of ERRANT in the emulation of real mobile networks in terms of average behavior and obtained variability. We also show the limitations of a simple emulation, and of other freeware approaches versus ERRANT. Finally, we show two practical use cases to demonstrate the benefits of a dynamic emulation in understanding the performance of web browsing and video streaming. To run new measurement campaigns and create new models, we provide guidelines along with the required open-source code.

Modeling QoE for Buffered Video Streaming in Interference-Limited Cellular Networks

Mobile networks have to cope with an ever increasing demand for video streaming, an application that imposes high quality requirements for user satisfaction. In this paper, we present an analytical model to calculate two important quality measures for streaming traffic, namely the video startup delay distribution and the buffer starvation probability. The queuing-theoretic model differs from related work by incorporating data flow dynamics of the considered cell, as well as the dynamics of fluctuating interference from neighboring cells, with the goal of accurately representing a multi-cellular environment. In this regard, we propose a finite-volume method to approximate the solution of the involved system of partial differential equations, where other approaches from comparable work were faced with numerical problems. We also evaluate two simplified versions of the model, where only the interference dynamics or all coupling terms are omitted, respectively. The model allows us to study the impact of different parameters on buffered video streaming performance. Additionally, we propose a user-centric metric to measure quality of experience (QoE). To the best of our knowledge, our approach is novel and has not been covered by comparable work. The presented results can help to design future cellular networks with enhanced video streaming experience.

Hardware-Assisted, Low-Cost Video Transcoding Solution in Wireless Networks

Wireless video streaming has become an extremely popular application in recent years. Internet video streaming to mobile devices, however, faces several challenges, e.g., unstable wireless connections, long latency, high jitter and etc. Bitrate adaptive streaming and video transcoding solutions are widely used to address the above-mentioned issues, however, there are still several shortcomings of these approaches. Such challenges hinder providing satisfactory quality of video streaming service to the mobile users. We propose a hardware-assisted, real-time video transcoding solution implemented on a commercial off-the-shelf device, Raspberry Pi. We employ the software and hardware coupled architecture in order to improve the performance/quality of video streaming and enhance the user satisfactions in wireless network. Our video transcoding solution can be applied to both the downlink and uplink streaming: for downlink stream, it can provide agile bitrate adaptation to sudden network dynamics and enhance video quality by running our transcoding solution at the wireless edge. It can be used to uplink stream for broadcasting live streams in real-time. We present the design and implementation of our video transcoding system in both cases with practical scenarios. The evaluation results reveal that our transcoding solution enhances the performance of video streaming compared with other adaptive bitrate streamings and it provides higher video quality without causing rebuffering or video stall. We bridge the gap between the wireless channel capacity and the video quality while providing a better streaming experience to end user.

Lightweight Online Profiling-Based Configuration Adaptation for Video Analytics System in Edge Computing

Video Analytics System has emerged as a promising technology to realize deep neural network based intelligent applications for video streams. Its objective is to maximize the video analytics performance of video streams, such as accuracy, while utilizing the limited computing resource capacity efficiently. Existing video analytics systems attempt to adapt configurations to optimize resource-accuracy tradeoffs under limited resource capacity. Especially, several works recently propose a configuration adaptation algorithm based on online profiling to overcome the inefficiency of one-time offline profiling caused by the dynamics of the configuration's impact on video analytics accuracy. However, their systems are inefficient or limited to process the video analytics of multiple video streams in a GPU-enabled edge server. Furthermore, their online profiling methods still leads to a high profiling cost. In this paper, we design a video analytics system to adapt configurations for optimizing the resource-accuracy tradeoffs of multiple video streams with respect to frame rate and resolution fully under limited resource capacity of a GPU-enable edge server. In addition, we propose a lightweight online profiling method, utilizing the underlying characteristics of the video objects. Then, based on it, we propose a configuration adaptation algorithm to find the best configuration of each video stream and minimize accuracy degradation of multiple video streams under limited resource capacity of a GPU-enable edge server. To evaluate the proposed algorithm, we use a subset of surveillance videos and annotations from VIRAT 2.0 Ground dataset. The experimental results show that, in a GPU-enabled edge server, our video analytics system achieves the optimal configuration adaptation on resource-accuracy tradeoffs and our algorithm reduce the profiling cost of existing systems significantly while achieving the video analytics performance comparable to them.

An Adaptive Quality Switch-aware Framework for Optimal Bitrate Video Streaming Delivery

Given a large number of online video viewers, video streaming, over various networks, is important communication technology. The multitude of viewers makes it challenging for service providers to provide a good viewing experience for subscribers. Video streaming capabilities are designed based on concepts including quality, viewing flexibility, changing network conditions, and specifications for different customer devices. Adjusting the quality levels, and controlling various relevant parameters to stream the video content with good quality and without interruption is vital. This paper proposes an adaptive framework to balance the average video bitrates with respect to appropriate quality switches, making the transition to higher switches more seamless. The quality adaptation scheme increases the bitrates to the maximum value at their current quality switch before shifting to a higher level. This reduced switching times between levels and guarantees the stability of viewing and avoids interruptions. The use of a dynamic system ensures optimal performance, by controlling system parameters and making the algorithm more tunable. We built the system using an open-source DASH library (Libdash) with QuickTime player, studied the video load changes on two performance parameters, Central Processing Unit and Memory usages that have a high impact on multimedia quality. Consequently, the values of parameters that affected the performance of video streaming could be decreased, permitting users to regulate the parameters according to their preferences. Further, reducing the switching levels will reduce the overloads that occur while transferring from one level to another.

Dissecting the Performance of VR Video Streaming through the VR-EXP Experimentation Platform

To cope with the massive bandwidth demands of Virtual Reality (VR) video streaming, both the scientific community and the industry have been proposing optimization techniques such as viewport-aware streaming and tile-based adaptive bitrate heuristics. As most of the VR video traffic is expected to be delivered through mobile networks, a major problem arises: both the network performance and VR video optimization techniques have the potential to influence the video playout performance and the Quality of Experience (QoE). However, the interplay between them is neither trivial nor has it been properly investigated. To bridge this gap, in this article, we introduce VR-EXP, an open-source platform for carrying out VR video streaming performance evaluation. Furthermore, we consolidate a set of relevant VR video streaming techniques and evaluate them under variable network conditions, contributing to an in-depth understanding of what to expect when different combinations are employed. To the best of our knowledge, this is the first work to propose a systematic approach, accompanied by a software toolkit, which allows one to compare different optimization techniques under the same circumstances. Extensive evaluations carried out using realistic datasets demonstrate that VR-EXP is instrumental in providing valuable insights regarding the interplay between network performance and VR video streaming optimization techniques.

ITeleScope: Softwarized Network Middle-Box for Real-Time Video Telemetry and Classification

Video continues to dominate network traffic, yet operators today have poor visibility into the number, duration, and resolutions of the video streams traversing their domain. Current approaches are inaccurate, expensive, or unscalable, as they rely on statistical sampling, middle-box hardware, or packet inspection software. We present {\em iTelescope}, the first intelligent, inexpensive, and scalable SDN-based solution for identifying and classifying video flows in real-time. Our solution is novel in combining dynamic flow rules with telemetry and machine learning, and is built on commodity OpenFlow switches and open-source software. We develop a fully functional system, train it in the lab using multiple machine learning algorithms, and validate its performance to show over 95\% accuracy in identifying and classifying video streams from many providers including Youtube and Netflix. Lastly, we conduct tests to demonstrate its scalability to tens of thousands of concurrent streams, and deploy it live on a campus network serving several hundred real users. Our system gives unprecedented fine-grained real-time visibility of video streaming performance to operators of enterprise and carrier networks at very low cost.

Efficient Mobile Video Streaming via Context-Aware RaptorQ-Based Unequal Error Protection

Mobile video streaming systems typically apply the forward error correction (FEC) at the application layer to cope with packet-level transmission errors, which complements the bit-level correction mechanisms at the physical layer. However, most existing works fail to exploit the block-level dependencies in both intra and interframe coding modes of a single-layer compressed video, and thus are less efficient for the prevailing H.264/AVC and/or H.265/HEVC compatible single-layer video application. To this end, we propose a low-complexity FEC, i.e., context-aware RaptorQ (CA-RQ) with unequal error protection (UEP), to improve the error recovery performance of the single-layer mobile video streaming, through incorporating the block-level dependencies in the compressed video data. We use a packet-level video transmission distortion model that considers the dependencies in both spatial and temporal domains, to quantify the importance of video packets within a group of pictures (GoP). The compressed video packets are categorized and grouped into several classes according to their importance to construct the CA-RQ code with the UEP property. We provide a theoretical analysis on redundancy allocation bounds to demonstrate the superior performance of proposed CA-RQ over the standard RaptorQ code. In the meantime, extensive simulations have shown that our scheme not only offers much better subjective visual quality with less than 50% additional redundant symbols as compared to the Macroblock-Based UEP (MB-UEP) scheme, but also outperforms the MB-UEP and classical equal error protection (EEP)-based schemes, by a 0.45% $\sim$ 5.71% and 0.94% $\sim$ 6.78% margin, respectively, in reconstructed quality evaluated using the structural similarity (SSIM) index, across a reasonable range of redundancy proportions.

Joint Coding and Multicast Subgrouping Over Satellite-eMBMS Networks

Mobile satellite services are extremely important when continuity of service is requested while the user is moving over a wide area. A notable example is represented by live TV services, or more in general by the Multimedia Broadcast/Multicast Services (MBMSs) and its evolved version enhanced MBMS. In this paper, we present the combined use of multicast resource allocation schemes based on subgrouping and application layer joint coding to enhance the performance of live video streaming in mobile satellite systems. The reference architecture foresees a satellite-based long term evolution transmission using orthogonal frequency division multiple access in the forward link. The results show that the combined use of multicast subgrouping resource allocation and application layer joint coding allows high-throughput transmissions with satisfactory user quality of experience of the received video over different satellite channel propagation environments.

On the Performance of Dynamic Adaptive Video Streaming under Active Bluetooth Connection

On the Performance of Dynamic Adaptive Video Streaming under Active Bluetooth Connection

ArOMA: An SDN based autonomic DDoS mitigation framework

Distributed Denial of Service (DDoS) attacks have been the plague of the Internet for more than two decades, despite the tremendous and continuous efforts from both academia and industry to counter them. The lessons learned from the past DDoS mitigation designs indicate that the heavy reliance on additional software modules and dedicated hardware devices seriously impede their widespread deployment. This paper proposes an autonomic DDoS defense framework, called ArOMA, that leverages the programmability and centralized manageability features of Software Defined Networking (SDN) paradigm. Specifically, ArOMA can systematically bridge the gaps between different security functions, ranging from traffic monitoring to anomaly detection to mitigation, while sparing human operators from non-trivial interventions. It also facilitates the collaborations between ISPs and their customers on DDoS mitigation by logically distributing the essential security functions, allowing the ISP to handle DDoS traffic based on the requests of its customers. Our experimental results demonstrate that, in the face of DDoS flooding attacks, ArOMA can effectively maintain the performance of video streams at a satisfactory level.