Video Content Delivery Research Articles

OTT Platform

OTT platform stands for "over-the-top," which refers to the delivery of video, audio, and other media content over the internet directly to users without the need for traditional cable or satellite TV service providers. OTT platform services allow users to access and consume content anytime and anywhere, on multiple devices such as smart phones, tablets, smart TVs, and streaming devices. Examples of popular OTT platform services include Netflix, Amazon Prime Video, Hulu, Disney+, HBO Max, and YouTube TV. These services offer a wide range of content, including TV shows, movies, original programming, and live sports events. Many OTT platform services offer a subscription-based model, where users pay a monthly or annual fee to access the content. Some services also offer ad-supported content or pay-per- view options. The rise of OTT platform services has disrupted the traditional media landscape, with many consumers cutting the cord on cable and satellite TV and opting for OTT platform services instead. The convenience, affordability, and accessibility of OTT platform services have made them increasingly popular, and the industry is expected to continue to grow in the coming years. New OTT platform can integrate interactive features such as live chats, comments, or social media integration, enabling users to engage with contents and other viewers. Like all OTT platforms, new ones can provide users with the flexibility to watch content on demand, anytime and anywhere.

Dynamic Clustering for Low-Delay Delivery of Video Content Cached in MEC Servers

For the purpose of video caching and low-delay video delivery to the end-users, multiaccess edge computing (MEC) servers are commonly grouped into clusters to efficiently exploit the limited storage resources of the MEC servers. In this article, we first introduce a methodology for analysis of the video delivery delay using the queuing theory. Our analysis shows that the video delivery delay is mainly affected by the arrival rate of the video requests. Furthermore, we show a tradeoff between the ratio of videos found in the MEC servers within the same cluster and the transmission delay of video contents. To reduce the video delivery delay, we propose a dynamic MEC server clustering (DyMECC) algorithm that determines the cluster size at each time interval by solving analytically derived equations considering the actual arrival rate of video requests. It also acts as a congestion avoidance mechanism for the communication interfaces among the MEC servers. Via simulations, we show that the DyMECC reduces the video delivery delay about 15% in light load conditions and by more than five times in heavy load conditions compared to state-of-the-art works while also reduces the load of the communication interfaces among the MEC servers by more than 45%.

SEC-DEC: A Social Trust-Based Video-Centric Network Leveraging Secure Device-Edge-Cloud Collaborations

Omnipresent high-resolution surveillance cameras and handy touchscreens have changed our daily interactions with the connected world permanently. As high-definition video streams are aggressively reshaping our Internet surfing experience, severe challenges (e.g., responsiveness, cybersecurity) are also confronted by legacy cellular infrastructures. How to efficiently and securely stream the explosively growing video data will remain a vital issue for future wireless communications systems. Considering the restrictive element heterogeneity, resource bottleneck, setting dynamics, and data security, SEC-DEC, a secure social trust-based video-centric network, is proposed to screen both the logical and social heterogeneity among all types of network elements, leveraging social networking analytics and machine learning to perceive and evaluate both the vertical and horizontal relationships among all nodes. It also exploits the secure device-edge-cloud collaboration in physical, social, and service domains to realize efficient but reliable video content delivery. The context awareness, trust management, and collaborative distribution provided by SEC-DEC can effectively handle the channel dynamics, link complexity, and medium heterogeneity to enhance the security performance, QoE guarantee, and throughput of wireless infrastructures for video data traffic.

Editorial: Video Content Production and Delivery Over IP Networks and Distributed Computing Facilities

Editorial: Video Content Production and Delivery Over IP Networks and Distributed Computing Facilities

High Throughput JPEG 2000 for Video Content Production and Delivery Over IP Networks

ITU-T Rec T.814 | IS 15444-15, known as High Throughput JPEG 2000, or simply HTJ2K, is Part-15 in the JPEG 2000 series of standards, published in 2019 by the ITU and ISO/IEC. JPEG 2000 Part-1 has long been used as a key component in the production, archival and distribution of video content, as the distribution format for Digital Cinema, and an Interoperable Master Format from which streaming video services are commonly derived. JPEG 2000 has one of the richest feature sets of any coding standard, including scalability, region-of-interest accessibility and non-iterative optimal rate control. HTJ2K addresses a long-standing limitation of the original JPEG 2000 family of standards: relatively low throughput on CPU and GPU platforms. HTJ2K introduces an alternative block coding algorithm that allows extremely high processing throughputs, while preserving all other aspects of the JPEG 2000 framework and offering truly reversible transcoding with the original block coded representation. This paper demonstrates the benefits that HTJ2K brings to video content production and delivery, including cloud-based processing workflows and low latency video content streaming over IP networks, considering CPU, GPU and FPGA-based platforms. For non-iterative optimal rate control, HTJ2K encoders with the highest throughputs and lowest hardware encoding footprints need a strategy for constraining the number of so-called HT-Sets that are generated ahead of the classic Post-Compression Rate-Distortion optimization (PCRD-opt) process. This paper describes such a strategy, known as CPLEX, that involves a second (virtual) rate-control process. The novel combination of this virtual (CPLEX) and actual (PCRD-opt) processes has many benefits, especially for hardware encoders, where memory size and memory bandwidth are key indicators of complexity.

Analysis of MP4 Videos in 5G Using SDN

Current video content providers require a huge demand for network resources to obtain the best quality of experience from the end-users perspective. This measure can be achieved if the network can anticipate itself to different existing issues (bandwidth problems, etc.). Due to traditional Internet Protocol networks cannot handle this problem due to the rigidity of its architecture, software defined networks is introduced as a potential answer for resolving the existing challenges in video content delivery decoupling the control plane and data plane. Utilizing the unified perspective of the network, it is possible to develop a monitoring tool obtaining the network metrics to be used afterwards. In this paper, we propose the application of different clustering algorithms for monitoring software defined networks to validate the number of statistics queries decrements using the data and error rates to improve the network traffic and to reduce the overload. The simulations demonstrate that the expectation-maximization algorithm highly reduces the monitoring queries maintaining the best data and error rate values in the video delivery.

Biliary Atresia Screening in India-Strategies and Challenges in Implementation.

To assess the utilization of SCC implemented in southern India and the effect on SCC utilization of face-to-face verbal education versus video-based content delivery. The study included newborns with postnatal age of less than 2 wk at discharge. Mothers were administered SCCs and provided standardized verbal or video health education based on the time-period of enrollment. Home based monitoring of stool color and return of SCC on postnatal day 21 was advised. Telephone surveys were conducted to identify SCC use among families that did not return the SCC by post. Of the 2254 newborns enrolled, 1130 were in the verbal-counseling group and 1124 in the video-counseling group. No newborns with pale stools and biliary atresia were identified. SCC return rates were 3.8% and 2.8%. Comparing the verbal and video-counseling groups, there were no differences in the conservative (81.8% vs. 81.5%) and optimistic estimates (97.1% vs. 97.3%) of SCC utilization rates. Mothers with better educational status had higher optimistic estimates of SCC utilization. The use of a validated SCC in Tamil with standardized information delivery leads to good utilization rates in southern India, with video content delivery being as effective as face-to-face verbal content delivery. SCC return by post is not a feasible mode of identification of card use. The study is registered under Clinical Trials Registry- India (CTRI/2018/01/011285).

Link Reliable On-Demand Multipath Distance Vector Routing for Reliable Video Streaming in MANET

Background: Due to the generic feature of AOMDV, it is widely used in Long-Term Evolution (LTE) networks such as the Internet which requires new challenges to deploy conversational real-time applications like VoIP (Voice over IP) and video conferencing. Objective: In AOMDV, the multiple routes between any source and destination pair are selected based on minimal hop count which does not ensure reliable video content delivery as the communicating nodes along the paths are prone to link failures and route breaks. Methods: To overcome such problems, the Link Reliable On-demand Multipath Distance Vector (LROMDV) is proposed by modifying the Ad hoc On-demand Multipath Distance Vector (AOMDV). Results: The LROMDV uses an enhanced Cumulative Expected Transmission Count (enh-CETX) for selecting multiple routes between any source and destination pair to avoid link failures and route breaks in Long-Term Evolution (LTE) networks. Conclusion: The performance of LROMDV on H.264/MPEG-4 AVC video streaming under both Distributed Coordination Function (DCF) and Enhanced Distributed Coordination Function (EDCF) using NS2.34 and Enhanced EvalVid framework is evaluated and the simulation result shows the effectiveness of LROMDV.

Optimized video content delivery for mobile networks using the transformative computing model

It is a challenge for future mobile networks to cope with enormous video traffic due to bottlenecks of the backhaul capacity and spectrum shortages. To address this situation, we study a content delivery solution for video-on-demand (VoD) applications in orthogonal frequency division multiplexing (OFDM)-based mobile networks in this paper. As done in some transformative computing models, the proposed solution firstly collects the user and environment information, and then computes a global-optimized video delivery scheme based on the collected information. The video delivery scheme is orchestrated using a novel video delivery model that implements deep cooperation among video users and device-to-device (D2D) communications. Several orchestration algorithms are proposed to improve the performance of video delivery.

Cross-layer DASH-based multipath video streaming over LTE and 802.11ac networks

Dynamic Adaptive Streaming over Http (DASH) -based video streaming applications are becoming increasingly prevalent over the mobile Internet. Many efforts have been made to optimize their performances. Multipath video streaming that simultaneously utilizes multiple wireless networks for video content delivery is a common method. Another effective approach is the cross-layer video streaming optimization that jointly takes the parameters at different protocol layers into account. However, multipath streaming schemes mainly focus on how to efficiently utilize multiple wireless networks and the collaboration of parameters at different layers in each network is neglected. Likewise, the cross-layer schemes normally optimize the parameters at different layers in purely one network without fully utilizing the aggregated bandwidths of multiple available wireless networks. Therefore, both of them are sub-optimal and might suffer from degrading performance. In this paper, we propose a joint Cross-layer DASH-based multipath video streaming scheme that takes advantage of bandwidth aggregation of multiple wireless networks and further improves the performance by optimizing the different layers’ parameters in each network with a cross-layer manner. In the proposed scheme, the LTE and 802.11ac networks are adopted. The bitrate of DASH-based video chunk at application layer, the rate allocation among networks and the Modulation and Coding Scheme (MCS) at physical layers in LTE and 802.11ac downlink are jointly optimized. We also compare our proposed scheme to state-of-the-art schemes using trace-driven experiments. Experimental results show that our proposed scheme outperforms state-of-the-art schemes in terms of PSNR, normalized QoE, and balance between video bitrate and rebuffering penalty.

Empirical Results for High-definition Video and Augmented Reality Content Delivery in Hyper-connected Cars

Abstract Software architecture and applications for the connected car can process and share a variety of digital content, among which high-definition video and augmented reality (AR) content, toward enhanced driving assistance, navigation and infotainment systems and services. However, several technical challenges need to be overcome to make such systems and services viable and efficient, including dealing effectively with a variety of types of systems, devices and platforms, either installed inside the vehicle or represented by the personal mobile and wearable devices of the drivers and passengers. In this paper, we outline these technical challenges and propose a software solution in the form of an event-based middleware layer by modeling the smart, connected car as a specific type of a smart environment. We employ an adapted version of Euphoria, a recently introduced software architecture for general-purpose smart environments, to implement asynchronous communications among heterogeneous input/output devices inside the vehicle. We also adapt Euphoria to fit into the four-layer infrastructure model of the connected car. We conduct a technical evaluation of the request-response time performance achieved with the Euphoria middleware for streaming digital content from 1 Mbps (360p@30fps) to 32 Mbps (4K@30fps) on various devices, either integrated in the vehicle, not integrated but used inside the vehicle and devices outside the vehicle (the control condition). Our results show effective live streaming achieved for 2K content at 30fps with the 600 Mbps network (i.e., the connected car) and for 4K content at 30fps with the 1.7 Gbps network envisioned for hyper-connected vehicles. These results open up opportunities for high-definition video and AR applications in the automotive industry.

Quality assessment experiment for 4k SVT-AV1 video format

The industrial consortium AOMedia has recently developed the AV1 (AOMedia Video 1) format considered as a successor of VP9. One of the main goals with the video coding and compression format is to enable efficient Web based video content delivery, as in the case of OTT (Over-the-top) services. Nowadays, there are different AV1 software implementations available for experimental analysis besides the reference tool libaom. The Scalable Video Technology for AV1, or SVT-AV1 CODEC, is oriented towards further optimizations for specific Intel hardware. In this paper, we have performed a video quality assessment experiment with some of the SVT-AV1 implementations using 4k like video. The results show the advantages of SVT-AV1 format, as well as its performance for different quantization factors.

Scalable Hash From Triplet Loss Feature Aggregation For Video De-duplication

The producing, sharing and consuming life cycle of video content creates massive amount of duplicates in video segments due to variable bit rate representation and fragmentation in the playbacks. The inefficiency of this duplicates to storage and communication motivate researchers in both academia and industry to come up with computationally efficient video deduplication solutions for storage and CDN providers. Moreover, the increasing demands of high resolution and quality aggravate the status of heavy burden of cluster storage side and restricted bandwidth resources. Hence, video de-duplication in storage and transmission is becoming an important feature for video cloud storage and Content Delivery Network (CDN) service providers. Despite of the necessity of optimizing the multimedia data de-duplication approach, it is a challenging task because we should match as many as possible duplicated videos under not removing videos by mistake. The current video de-duplication schemes mostly relies on the URL based solution, which is not able to deal with non-cacheable content like video, which the same piece of content may have totally different URL identification and fragmentation and different quality representations further complicate the problem. In this paper, we propose a novel content based video segmentation identification scheme that is invariant to the underlying codec and operational bit rates, it computes robust features from a triplet loss deep learning network that captures the invariance of the same content under different coding tools and strategy, while a scalable hashing solution is developed based on Fisher Vector aggregation of the convolutional features from the Triplet loss network. Our simulation results demonstrate the great improvement in terms of large scale video repository de-duplication compared with state-of-the-art methods.

CDN Slicing over a Multi-Domain Edge Cloud

We present an architecture for the provision of video Content Delivery Network (CDN) functionality as a service over a multi-domain cloud. We introduce the concept of a CDN slice, that is, a CDN service instance which is created upon a content provider's request, is autonomously managed, and spans multiple, potentially heterogeneous, edge cloud infrastructures. Our design is tailored to a 5G mobile network context, building on its inherent programmability, management flexibility, and the availability of cloud resources at the mobile edge level, thus close to end users. We exploit Network Functions Virtualization (NFV) and Multi-access Edge Computing (MEC) technologies, proposing a system which is aligned with the recent NFV and MEC standards. To deliver a Quality-of-Experience (QoE) optimized video service, we derive empirical models of video QoE as a function of service workload, which, coupled with multi-level service monitoring, drive our slice resource allocation and elastic management mechanisms. These management schemes feature autonomic compute resource scaling, and on-the-fly transcoding to adapt video bit-rate to the current network conditions. Their effectiveness is demonstrated via testbed experiments.

Energy-Efficient Proactive Caching for Adaptive Video Streaming via Data-Driven Optimization

Proactive caching in mobile-edge computing (MEC) networks is promising to handle the ever-increasing demand for wireless video services, and transcoding at MEC servers further improves the flexibility of video content delivery. However, how to effectively conduct caching for adaptive bitrate streaming poses great challenges due to the uncertainty of user preferences. The caching decisions also have a profound impact on the system energy efficiency since they may change the video delivery modes. In this article, by integrating caching, transcoding, and backhaul retrieving in a MEC-enabled adaptive streaming system, we propose a holistic solution to jointly determine the caching of bitrate-aware files and the scheduling of video requests in an energy-efficient manner. Specifically, we leverage a data-driven approach to characterize the uncertainty of real request arrivals. Based on the uncertainty model, we formulate a data-driven risk-averse optimization to derive a robust strategy for caching and delivery scheduling, which is a two-stage stochastic mixed-integer programming (SMIP) with the goal of minimizing the total expected energy consumption. We also develop feasible solutions and conduct extensive simulations on real-world data sets. The results validate the effectiveness of the proposed scheme in both the energy efficiency and the cache hit ratio.

Cooperative Caching and Transmission in CoMP-Integrated Cellular Networks Using Reinforcement Learning

Mobile network caching (MNC) and Coordinated MultiPoint (CoMP) joint transmission (JT) techniques are expected to play a significant role in future networks. Yet, when considering caching in a CoMP-integrated network where both single transmission (ST) and JT are permitted, some challenges need to be tackled. In this paper, we propose a cooperative online caching strategy to realize autonomous transmission-aware content caching and updating for video content delivery. Different from existing works, this paper considers practical time-varying user request patterns, and exploits storage-level and transmission-level cooperation to jointly optimize content caching under dynamic user demands for JT and ST. The former helps reduce caching redundancy and the latter helps BSs create CoMP-JT opportunities to improve cell-edge throughput. We formulate the content caching problem to be a Markov decision process (MDP), in which the system reward is defined as the delay reduction after performing a caching action. The goal is to maximize the system reward. Then, we develop a reinforcement learning (RL)-based online learning algorithm to search the optimal caching policy. To overcome the “curse of dimensionality,” the designed RL-based algorithm is extended with linear approximation. The system performance in terms of delay and cache hit rate is evaluated in the simulations, and the results demonstrate the effectiveness of the proposed strategy.

Automatic Annotation of Instructional Videos

Multimedia has a significant role in communicating the information and a large amount of multimedia repositories make the browsing, retrieval and delivery of video contents. For higher education, using video as a tool for learning and teaching through multimedia application is a considerable promise. To extract the audio information from the visual content is a very challenging because it needs the extraction of high level semantic information from low level visual data. The summarization technique for videos has been proposed to improve the browsing faster for large video collections to produce more efficient content indexing and access. The proposed video summarization system for instructional videos initially separates videos into audio track and then converted into text transcript. The text transcripts are pre-processed and extracted the important relevant keywords which can be used for indexing the video file. The automatic annotation of instructional videos reduced the summary length and also preserved the semantic of the videos. The research indicates that the students found these summarized content is very helpful for preparing and reviewing the lecture material and also for the preparation during their examination.

Social- and Content-Aware Prediction for Video Content Delivery

An ever-increasing number of videos in mobile social networks increase the waiting time of video downloading. Video prefetching is a viable way to save time during video downloading, while prefetching all the videos is resource wasteful if the videos are not watched. Therefore, careful prediction of whether a user will watch a video is critical for efficient video content delivery. Most existing work on social media recommendation focuses on the top-N problem to recommend multiple videos. In this paper, we deal with the problem of predicting whether a video will be watched by a user for efficient video content delivery in mobile social networks. We propose a Social- and Content-aware Video content delivery Prediction method (SCVP) for the problem by capturing the intrinsic relationship among users and videos. We design five metrics to estimate the factors of active degree of users, social tier between users, similarity between videos, similarity between user interest and video content, and video popularity. We then use combined prediction for the video content delivery prediction to incorporate the impacts of the five factors on the prediction. Finally, we conduct experiments through simulations. Experimental results demonstrate that the proposed method SCVP can predict whether a video is watched by users with high accuracy.

A Survey on Adaptive 360° Video Streaming: Solutions, Challenges and Opportunities

Omnidirectional or 360° video is increasingly being used, mostly due to the latest advancements in immersive Virtual Reality (VR) technology. However, its wide adoption is hindered by the higher bandwidth and lower latency requirements than associated with traditional video content delivery. Diverse researchers propose and design solutions that help support an immersive visual experience of 360° video, primarily when delivered over a dynamic network environment. This paper presents the state-of-the-art on adaptive 360° video delivery solutions considering end-to-end video streaming in general and then specifically of 360° video delivery. Current and emerging solutions for adaptive 360° video streaming, including viewport-independent, viewport-dependent, and tile-based schemes are presented. Next, solutions for network-assisted unicast and multicast streaming of 360° video content are discussed. Different research challenges for both on-demand and live 360° video streaming are also analyzed. Several proposed standards and technologies and top international research projects are then presented. We demonstrate the ongoing standardization efforts for 360° media services that ensure interoperability and immersive media deployment on a massive scale. Finally, the paper concludes with a discussion about future research opportunities enabled by 360° video.

SecP2PVoD: a secure peer-to-peer video-on-demand system against pollution attack and untrusted service provider

The Peer-to-Peer video-on-demand (P2P-VoD) streaming has become widespread in recent years. Unlike the traditional client-server model based video-streaming, the P2P-VoD leverages the peer’s capacity of upload bandwidth for the delivery of video contents in a distributed network. The P2P environment is susceptible to various security threats, in which the pollution attack is one of the potentially destructive threats. Moreover, video streaming is prone to other security challenges, such as authenticity, confidentiality, authorization, and integrity. There have been discussed four possible protection to the pollution attack: blacklisting, hash verification, traffic encryption, and chunk signing. In this paper, we present escrow-free identity-based signcryption (EF-IDSC) scheme for secure data transmission scheme in P2P-VoD streaming with an untrusted service provider. The proposed system enables a peer to establish a session key with other peer using the asymmetric key algorithm. The security analysis shows that the proposed P2P-VoS system prevents pollution attacks under well-known random oracle model and achieves privacy, confidentiality, and subscriber authentication simultaneously. The experimental evaluation shows that the proposed scheme has better computation and communication costs as compared to the related schemes.