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The metaverse has influenced the development of science and technology since it was proposed. While artificial intelligence (AI) is still one of the important technologies to solve problems in metaverse. The success of AI in various fields and the development of information technology make the integration of sports industry and AI an inevitable trend. Currently, the traditional physical education is experiencing qualitative changes, and the demand of integrating AI and metaverse into physical education is becoming more and more obvious. Football teaching is an important part of physical education, and Virtual Reality (VR) has the characteristics of immersion, interaction and imagination, which can build virtual and realistic football teaching process. In this study, through 360-degree panoramic VR football teaching videos empowered by the metaverse and K-means algorithm based on machine learning under AI, we study the strategies for improving the quality of football teaching in the mobile Internet environment. Therefore, we propose K-means based optimized 360-degree panoramic VR football teaching video delivery strategy. In addition, we conduct simulation experiments under content delivery networks simulator, and the simulation results show that the proposed strategy is superior to the baselines in terms of proxy server hit ratio, byte hit ratio, mean response time and students quality of experience. Moreover, through the 360-degree panoramic VR football teaching videos learning, students can intuitively analyze the actions and improve the teaching quality. The reconstruction of football teaching environment is beneficial to promote the combination of football teaching and smart learning. [ABSTRACT FROM AUTHOR]

When working with the Wireless Access in Vehicular Environment (WAVE) protocol stack, the multi-channel operation mechanism of the IEEE 1609.4 protocol may impact the overall network performance, especially when using video streaming applications. In general, packets delivered from the application layer during a Control Channel (CCH) time slot have to wait for transmission until the next Service Channel (SCH) time slot arrives. The accumulation of packets at the beginning of the latter time slot may introduce additional delays and higher contention when all the network nodes try, at the same time, to obtain access to the shared channel in order to send the delayed packets as soon as possible. In this work, we have analyzed these performance issues and proposed a new method, which we call SkipCCH, that helps the MAC layer to overcome the high contention produced by the packet transmission bursts at the beginning of every SCH slot. This high contention implies an increase in the number of packet losses, which directly impacts the overall network performance. With our proposal, streaming video in vehicular networks will provide a better quality of reconstructed video at the receiver side under the same network conditions. Furthermore, this method has particularly proven its benefits when working with Quality of Service (QoS) techniques, not only by increasing the received video quality but also because it avoids starvation of the lower-priority traffic. [ABSTRACT FROM AUTHOR]

IntroductionVideo service providers are moving from focusing on Quality of Service (QoS) to Quality of Experience (QoE) in their video networks since the users’ demand for high-quality video content is continually growing. By focusing on QoE, video service providers can provide their subscribers with a more personalized and engaging experience, which can help increase viewer satisfaction and retention. This focus shift requires not only a more sophisticated approach to network management and new tools and technologies to measure and optimize QoE in their networks but also a novel approach to video delivery operations.MethodsThis paper describes the components, interactions, and relationships of an algorithm factory for video delivery operation that assures high QoE for video streaming services. The paper also showcases the results of gradually implementing an algorithm factory in the video industry. Using a dataset from 2016 to 2022, we present the case of a European PayTV service provider that achieved improved performance measured by both objective and subjective metrics.ResultsThe use of an algorithm factory significantly improved the PayTV service provider’s performance. The study found a fivefold increase in the speed of critical incident resolution and a 59% reduction in the number of critical incidents, all while expanding the customer base and maintaining the same level of labor resources. The case also demonstrates a strong positive relation between the productivity measures of the PayTV operator and their survey-based quality ratings. These results underscore the importance of flawless QoS and operational excellence in delivering QoE to meet the evolving demands of viewers.DiscussionThe paper adds to the existing literature on relationships between operational efficiency, innovation, and subjective quality. The paper further offers empirical evidence from the PayTV industry. The insights provided are expected to benefit both traditional and over-the-top (OTT) video service providers in their quest to stay ahead in the rapidly evolving video industry. It may also translate to other service providers in similar industries committed to supporting high-quality service delivery.

When working with the Wireless Access in Vehicular Environment (WAVE) protocol stack, the multi-channel operation mechanism of the IEEE 1609.4 protocol may impact the overall network performance, especially when using video streaming applications. In general, packets delivered from the application layer during a Control Channel (CCH) time slot have to wait for transmission until the next Service Channel (SCH) time slot arrives. The accumulation of packets at the beginning of the latter time slot may introduce additional delays and higher contention when all the network nodes try, at the same time, to obtain access to the shared channel in order to send the delayed packets as soon as possible. In this work, we have analyzed these performance issues and proposed a new method, which we call SkipCCH, that helps the MAC layer to overcome the high contention produced by the packet transmission bursts at the beginning of every SCH slot. This high contention implies an increase in the number of packet losses, which directly impacts the overall network performance. With our proposal, streaming video in vehicular networks will provide a better quality of reconstructed video at the receiver side under the same network conditions. Furthermore, this method has particularly proven its benefits when working with Quality of Service (QoS) techniques, not only by increasing the received video quality but also because it avoids starvation of the lower-priority traffic.

The recent advances in multimedia technology have significantly expanded the range of audio–visual applications. The continuous enhancement of display quality has led to the emergence of new attributes in video, such as enhanced visual immersion and widespread availability. Within media content, the video signals are presented in various formats including stereoscopic/3D, panoramic/360°and holographic images. The signals are also combined with other sensory elements, such as audio, tactile, and olfactory cues, creating a comprehensive multi-sensory experience for the user. The development of both qualitative and quantitative Quality of Experience (QoE) metrics is crucial for enhancing the subjective experience in immersive scenarios, providing valuable guidelines for system enhancement. In this paper, we review the most recent achievements in QoE assessment for immersive scenarios, summarize the current challenges related to QoE issues, and present outlooks of QoE applications in these scenarios. The aim of our overview is to offer a valuable reference for researchers in the domain of multimedia delivery. • Summarizes immersion, interaction, and interconnection factors in video services. • Reviews QoE assessment methods for video services focusing on key experience factors. • Explores trends and challenges in QoE research for emerging video services. [ABSTRACT FROM AUTHOR]

Social networks change the way and approaches of video spread and promote range and speed of video spread, which results in frequent traffic blowout and a heavy load on the networks. The social and geographical communication efficiency determines the efficiency of video sharing, which enables the eruptible traffic to be offloaded in underlaying networks to relieve the load of networks and ensure the user quality of the experience. In this paper, we propose a novel geo-social-aware video edge delivery strategy based on the modeling of the social-geographical dynamic in urban area (GSVD). By investigating the frequency of sharing behaviors, social communication efficiency, and efficiency of social sub-network consisting of one-hop social neighbors of users, GSVD estimates the interactive and basic social relationship to calculate the closeness of the social relationship between mobile users. GSVD makes use of grid partition and coding subarea to express the geographical location of mobile users and designs a calculation method of coding-based geographical distance. GSVD considers the dynamic update of social distance and geographical location and designs a measurement of video delivery quality in terms of delivery delay and playback continuity. A strategy of video delivery with the consideration of adapting to social-geographical dynamic is designed, which effectively promotes the efficiency of video sharing. Extensive tests show how GSVD achieves much better performance results in comparison with other state of the art solutions. [ABSTRACT FROM AUTHOR]

In recent years, image and video super-resolution have gained attention outside the computer vision community due to the outstanding results produced by applying deep-learning models to solve the super-resolution problem. These models have been used to improve the quality of videos and images. In the last decade, video-streaming applications have also become popular. Consequently, they have generated traffic with an increasing quantity of data in network infrastructures, which continues to grow, e.g., global video traffic is forecast to increase from 75% in 2017 to 82% in 2022. In this paper, we leverage the power of deep-learning-based super-resolution methods and implement a model for video super-resolution, which we call VSRGAN+. We train our model with a dataset proposed to teach systems for high-level visual comprehension tasks. We also test it on a large-scale JND-based coded video quality dataset containing 220 video clips with four different resolutions. Additionally, we propose a cloud video-delivery framework that uses video super-resolution. According to our findings, the VSRGAN+ model can reconstruct videos without perceptual distinction of the ground truth. Using this model with added compression can decrease the quantity of data delivered to surrogate servers in a cloud video-delivery framework. The traffic decrease reaches 98.42% in total. [ABSTRACT FROM AUTHOR]

Information‐Centric Networking (ICN) has been widely applied into different fields, such as 5G, big data and new networking structures. This paper pays attention to the improvement of physical education, realizing the high‐performance online physical education based on Artificial Intelligence (AI). Currently, the online physical education is faced with a serious challenge, that is, the standard of smart video delivery cannot be reached, which causes that the physical learning effect of students through video has not been developed and approved well. In this paper, the physical education video is deployed at ICN structure. Then, based on ICN, this paper makes a video delivery ‐based case study for the online physical education optimization, where Reinforcement Learning (RL) is used for transmission optimization of video. Finally, this paper makes a simulation in terms of the proposed physical education optimization scheme, and the results demonstrate its feasibility and efficiency. [ABSTRACT FROM AUTHOR]

The huge amounts of network traffic generated by the ultra-high-definition video playback of super-large-scale video users results in tremendous pressure for front-haul and back-haul in the 5G network, which brings severely negative influence for the large-scale deployment and scalability of video systems and video delivery performance (e.g. transmission delay and packet loss) related to user quality of experience. The direct D2D communications between mobile devices with adjacent position in geographical area can offloading huge video traffic into the underlying networks, which reduces load of cellular base station in the edge networks and relieves traffic pressure in the core networks. In this paper, we propose a novel Social-Aware D2D Video Delivery Method based on Rapid Sample-Efficient Measurement of Mobility Similarity in 5G Ultra-Dense Network (DMSEM). By investigation for one-hop D2D pair relationship, DMSEM builds a social state transition model of user movement, which makes use of encounter duration and shared video length between encountered users to define the state transition condition. A cluster algorithm of encounter events is proposed, which achieves initial clusters of encounter events by calculating similarity between encounter events from the two aspects of both variation rate of geographical distance between mobile users and encounter duration time. DMSEM makes use of the Fuzzy C-Means to refine the initial clusters and extracts encounter patterns of mobile users. DMSEM designs a sample-efficiency rapid recognition algorithm of encounter pattern, which can use small number of encounter distance samples to achieve fast heuristic recognition of encounter pattern. Extensive tests show how DMSEM achieves better results in comparison with other state-of-the-art solutions in terms of packet loss rate, average freeze time, cache utilization, average bitrate, buffer level and control overhead.

In recent years, image and video super-resolution have gained attention outside the computer vision community due to the outstanding results produced by applying deep-learning models to solve the super-resolution problem. These models have been used to improve the quality of videos and images. In the last decade, video-streaming applications have also become popular. Consequently, they have generated traffic with an increasing quantity of data in network infrastructures, which continues to grow, e.g., global video traffic is forecast to increase from 75% in 2017 to 82% in 2022. In this paper, we leverage the power of deep-learning-based super-resolution methods and implement a model for video super-resolution, which we call VSRGAN+. We train our model with a dataset proposed to teach systems for high-level visual comprehension tasks. We also test it on a large-scale JND-based coded video quality dataset containing 220 video clips with four different resolutions. Additionally, we propose a cloud video-delivery framework that uses video super-resolution. According to our findings, the VSRGAN+ model can reconstruct videos without perceptual distinction of the ground truth. Using this model with added compression can decrease the quantity of data delivered to surrogate servers in a cloud video-delivery framework. The traffic decrease reaches 98.42% in total.

With the development of various mobile social applications, video data has already occupied a dominant proportion of mobile Internet traffic, and is still growing rapidly, which not only brings economic pressure to users, but also causes traffic explosion on Internet. Data delivery through mobile opportunistic networks is regarded as an efficient way to deal with this problem. But, due to the characteristics of video data and the mode of multi-copy routing, video opportunistic delivery will consume large amount of network resources, resulting in excessive transmission overhead and lower delivery quality. Fortunately, the strong aggregation of human activity gives us some inspiration, which can provide more space of deep optimization on video opportunistic transmission. Thus, in this paper, taking fully advantage of the connectivity of cluster formed by the gathering crowds, we propose a Video Opportunistic Replication scheme based on multi-player cooperative games, named VideoOR, which can achieve the double optimizations of transmission overhead and video delivery quality by scheduling the replication and forwarding of video packets under the guidance of Nash Equilibrium solution. Extensive simulations have been done based on the synthetic and real mobility traces, and the results show that our scheme can maximize the quality of reconstructed video and minimize the average replication times of each video packet. [ABSTRACT FROM AUTHOR]

The demand of media delivery services has increased with the popularity of social media and with the evolution of the user’s devices (i.e., smartphones, laptops, and tablets) pushing towards new contents distribution models. The coexistence of go-live and on-demand media content requires a combined broadcast/unicast delivery model with the efficient management of the wireless access as a key issue. A twofold target needs to be reached: optimizing the load balance among coexisting networks and offering adequate quality of service (QoS) to users. To achieve this target for mobile video service delivery over heterogeneous networks (HetNet) scenarios, this paper proposes a solution based on an additive logarithmic weighting (ALOW) algorithm combining received signal power, network load, packet delay, user’s equipment, and user’s credit budget. ALOW is optimized by means of a cooperative game theory (GATH) approach. The proposed solution, named ALOWGATH (i.e., ALOW + GATH), has been tested on realistic HetNet scenarios and compared to the state of the art of the network selection and balancing algorithms. Results show an improved performance in terms of throughput, satisfaction index and overall video quality delivered, with reduced computational complexity. [ABSTRACT FROM AUTHOR]

Introduction: Video service providers are moving from focusing on Quality of Service (QoS) to Quality of Experience (QoE) in their video networks since the users' demand for high-quality video content is continually growing. By focusing on QoE, video service providers can provide their subscribers with a more personalized and engaging experience, which can help increase viewer satisfaction and retention. This focus shift requires not only a more sophisticated approach to network management and new tools and technologies to measure and optimize QoE in their networks but also a novel approach to video delivery operations., Methods: This paper describes the components, interactions, and relationships of an algorithm factory for video delivery operation that assures high QoE for video streaming services. The paper also showcases the results of gradually implementing an algorithm factory in the video industry. Using a dataset from 2016 to 2022, we present the case of a European PayTV service provider that achieved improved performance measured by both objective and subjective metrics., Results: The use of an algorithm factory significantly improved the PayTV service provider's performance. The study found a fivefold increase in the speed of critical incident resolution and a 59% reduction in the number of critical incidents, all while expanding the customer base and maintaining the same level of labor resources. The case also demonstrates a strong positive relation between the productivity measures of the PayTV operator and their survey-based quality ratings. These results underscore the importance of flawless QoS and operational excellence in delivering QoE to meet the evolving demands of viewers., Discussion: The paper adds to the existing literature on relationships between operational efficiency, innovation, and subjective quality. The paper further offers empirical evidence from the PayTV industry. The insights provided are expected to benefit both traditional and over-the-top (OTT) video service providers in their quest to stay ahead in the rapidly evolving video industry. It may also translate to other service providers in similar industries committed to supporting high-quality service delivery., Competing Interests: OB and VK were employed at Divitel. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Molnar, Ferreira Pires, de Boer and Kovaleva.)

The caching-enabled networks, especially Information-Centric Networking (ICN), have attracted much attention from some global research communities, and the corresponding achievements have been highlighted in the field of video delivery. This paper studies more effective video delivery mechanism in order to guarantee better network performance based on two conceptions, i.e., the intermediate caching of popular video segments and the light delivery of identity object. At first, we evaluate the popularity under the dynamic environment, including descending modelling, ascending modelling and period modelling. Then, we propose identity-based light video delivery mechanism; in particular, we introduce Data Plane Development Kit (DPDK) to ensure that all video segments have the same size and to accelerate video transmission by bypassing the kernel. Finally, the simulation experiments are made based on the real YouTube dataset over CERNET network topology, and the results demonstrate that the proposed video delivery mechanism outperforms two the-state-of-the-art mechanisms in terms of cache hit ratio, routing hop count, delivery delay and network load. [ABSTRACT FROM AUTHOR]

A novel Key Quality Indicator for video delivery applications, XLR (piXel Loss Rate), is defined, characterized, and evaluated. The proposed indicator is an objective measure that captures the effects of transmission errors in the received video, has a good correlation with subjective Mean Opinion Scores, and provides comparable results with state-of-the-art Full-Reference metrics. Moreover, XLR can be estimated using only a lightweight analysis on the compressed bitstream, thus allowing a No-Reference operational method. Therefore, XLR can be used for measuring the quality of experience without latency at any network location. Thus, it is a relevant tool for network planning, specially in new high-demanding scenarios. The experiments carried out show the outstanding performance of its linear-dimension score and the reliability of the bitstream-based estimation. [ABSTRACT FROM AUTHOR]

This article explains how the distributed ledger technology that has successfully decentralized currencies can be used to enable a decentralized marketplace for digital content rights. Today, multiple centralized rights lockers from companies and consortiums offer similar but incompatible systems to register a consumer's video purchases and manage content rights. Each is representing a monopoly that centrally governs the content rights and is motivated to do so only as long as it remains profitable. Blockchain technology can be used to establish a permanent, secure, decentralized, and distributed registry for content registration, assignment, and archival of rights. The benefits of leveraging blockchain for video content distribution and content rights management include longevity that is independent of a single standard, company, or governing body as well as security of transactions with cryptographic guarantee of entitlements and grants of content licenses that cannot be changed but can be verified permanently. Lastly, the distributed and replicated nature of the peer-to-peer (P2P) network provides resiliency, eliminating a single point of failure like a company or governing body. This article will provide a novel system design that explains how a blockchain can be applied to not only manage content rights but to also secure content by enabling digital rights management (DRM) and playback control as an integral part of the decentralized network. It also details how to improve common ledger technology to overcome concerns of performance and abuse that are relevant to this application. [ABSTRACT FROM AUTHOR]

This paper proposes a video delivery strategy for dynamic streaming services which maximizes time-average streaming quality under a playback delay constraint in wireless caching networks. The network where popular videos encoded by scalable video coding are already stored in randomly distributed caching nodes is considered under adaptive video streaming concepts, and distance-based interference management is investigated in this paper. In this network model, a streaming user makes delay-constrained decisions depending on stochastic network states: 1) caching node for video delivery, 2) video quality, and 3) the quantity of video chunks to receive. Since wireless link activation for video delivery may introduce delays, different timescales for updating caching node association, video quality adaptation, and chunk amounts are considered. After associating with a caching node for video delivery, the streaming user chooses combinations of quality and chunk amounts in the small timescale. The dynamic decision making process for video quality and chunk amounts at each slot is modeled using Markov decision process, and the caching node decision is made based on the framework of Lyapunov optimization. Our intensive simulations verify that the proposed video delivery algorithm works reliably and also can control the tradeoff between video quality and playback latency. [ABSTRACT FROM AUTHOR]

With the increasing popularity of video delivery among mobile users, the problem of explosive traffic growth becomes more and more serious for the mobile wireless networks. To deal with this problem, in this paper we propose to exploit opportunistic transmission and the idea of crowdsourcing to offload mobile video traffic. Based on this idea, a Quality of VIDEO (QoV) oriented pricing incentive scheme, namely Vbargain, is proposed to stimulate mobile users to deliver video data collaboratively. More specifically, in our scheme, the network is regarded as a Virtual Market, and the video packets are treated as commodities, which are dynamically priced according to their expected marginal gains on the quality of reconstructed video; the process of video delivery is regarded as a sequence of packet transactions which are modeled as two-person cooperative games. Driven by the profit-hunting nature of mobile users, the video data is delivered from the source to its destination. In order to gain deep insight into our scheme, we also analyze the overhead and the influence of start-up capital on the performance of video delivered theoretically, and give its lower bound and upper bound. Our simulation results, based on both the synthetic and real-life traces of mobile users, verify the efficiency of our scheme and analysis. [ABSTRACT FROM AUTHOR]

In this paper, we first examine the causal relationship between the perceived video download speed and user engagement, while the speed has a very limited impact on views of short-length videos, a lower speed could significantly impair the viewing completion rate of medium and long-length videos. In addition, we observe that a view with an experienced speed close to half of the video bitrate lasts 10% of the video less in comparison to a similar view with a higher relative speed. At last, We also pointed out that crossing AS (Autonomous System) borders does not necessarily imply a higher likelihood of being a problem session, as only 22% of the AS pairs show a statistically significant impact on video download speed. [ABSTRACT FROM AUTHOR]

Resource management in emerging dense heterogeneous network environments (DenseNets) is a challenging issue. The employment of multicast transmissions in this scenario has potential to address the problems. On one hand, the large number of smart user mobile devices and user expectations for high-quality rich media services has determined a growing demand for network resources; in DenseNets, mobile users have to make the choice in terms of the network to connect to, in order to balance energy saving and delivery performance. On the other hand, the proliferation of user accesses to the existing and future network infrastructure will bring along with it the operators need for optimizing the radio resource usage. This paper proposes a hybrid unicast-multicast utility-based network selection algorithm (HUMANS), which offers the additional option of selecting multicast transmissions in the network selection process during video delivery. By serving users with good channel conditions via unicast transmissions and users with poor channel quality conditions via multicast, HUMANS allows outperforming other solutions in terms of outage percentage and average quality of transmission, in both low- and high-density scenarios. Most importantly, at the same time it guarantees operators a more efficient resource utilization. [ABSTRACT FROM AUTHOR]

Video delivery in Vehicular Ad-hoc NETworks has a great number of applications. However, multimedia streaming over this kind of networks is a very challenging issue because (a) it is one of the most resource-demanding applications; (b) it requires high bandwidth communication channels; (c) it shows moderate to high node mobility patterns and (d) it is common to find high communication interference levels that derive in moderate to high loss rates. In this work, we present a simulation framework based on OMNeT++ network simulator, Veins framework, and the SUMO mobility traffic simulator that aims to study, evaluate, and also design new techniques to improve video delivery over Vehicular Ad-hoc NETworks. Using the proposed simulation framework we will study different coding options, available at the HEVC video encoder, that will help to improve the perceived video quality in this kind of networks. The experimental results show that packet losses significantly reduce video quality when low interference levels are found in an urban scenario. By using different INTRA refresh options combined with appropriate tile coding, we will improve the resilience of HEVC video delivery services in VANET urban scenarios. [ABSTRACT FROM AUTHOR]

This paper has the following ambitious goal: to convince the reader that content caching is an exciting research topic for the future communication systems and networks. Caching has been studied for more than 40 years, and has recently received increased attention from industry and academia. Novel caching techniques promise to push the network performance to unprecedented limits, but also pose significant technical challenges. This tutorial provides a brief overview of existing caching solutions, discusses seminal papers that open new directions in caching, and presents the contributions of this special issue. We analyze the challenges that caching needs to address today, also considering an industry perspective, and identify bottleneck issues that must be resolved to unleash the full potential of this promising technique. [ABSTRACT FROM AUTHOR]

Video streaming has emerged as a killer application in today's Internet, delivering a tremendous amount of media contents to millions of users at any given time. Such a heavy traffic load demands an effective routing method. In this paper, an effective routing method, named GA-SDN, is developed based on software defined network (SDN) technique. To facilitate the researchers in this field to evaluate the video delivery quality over SDN, an evaluation framework and its associated source codes are provided. The framework integrates the H.264 Scalable Video coding streaming Evaluation Framework (SVEF) with the Mininet emulator. Through this framework, video processing researchers can evaluate their proposed coding algorithms in an SDN-enabled network emulator, while network operators or executives can evaluate the impact of real video streams on the developing network architectures or protocols. Experiment results demonstrate the usefulness of myEvalSVC_SDN and prove that GA-SDN outperforms traditional Bellman-Ford routing algorithm in terms of packet drop rate, throughput, and average peak signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

Due to the high attenuation of the water at high frequencies, underwater communications in freshwater are not being investigated so much. Many current underwater communication systems are based on acoustic or optical techniques. The use of electromagnetic (EM) waves in this medium, even in short distances, increases the bandwidth, which definitely implies a great advantage for video delivery. Related literature shows that the speed propagation and absorption coefficient in freshwater are independent of the working frequency of the transmitted signals. No work shows any temperature dependence with the electromagnetic waves propagation. In this paper, we study the EM wave's behavior when we vary the temperature at 2.4 GHz in underwater freshwater environments. We are going to study the signal behavior in this medium in order to deliver video images from the marine fish cages with the purpose of monitoring the fish activity. To carry out our study, we fix the water conditions and measure the maximum distance as a function of several network parameters such as the working frequency, data transfer rate, modulations and water temperature. Our results show that some combinations of temperature and working frequency generate better results than others. Finally, we will compare our results with the statements extracted from other works. [ABSTRACT FROM AUTHOR]

With the development of the Internet of Things, installation of smart mobile terminal in vehicle has become more and more popular, and consequently, how to provide services for the public utilizing vehicular ad hoc networks has aroused great interest in research and industrial areas. Among them, the kind of services supported by live video streaming attracts more attention because of its advantages. However, due to the high vehicular speed, frequent disconnection and dynamic topology, it's difficult to guarantee the low-delay delivery of real-time video data, and there is hardly any scheme that can deliver such kind of data with satisfied quality. In this paper, we take fully advantage of the existing wireless enabled traffic cameras, and propose a novel traffic-camera assisted routing for video delivery, which can deliver live video stream to mobile target vehicle with minimal start-up delay while satisfying the required visual quality and playback performance through optimal buffering points selection and intelligent relay among them. The trace-driven simulations demonstrate that our strategy outperforms existing solutions greatly. [ABSTRACT FROM AUTHOR]

The huge volume of contents generated from mobile ends has dramatically contributed to big data. Unfortunately, current packet scheduling policies in wireless networks for the underlying big data delivery greatly hinder the utilization of the contents. Specifically, the delivery of real-time big data requires running-time interactions with users, as well as variable bandwidth consumptions to maintain fine user experience. There is no previous work designed for this kind of traffic. To mitigate this gap, a thorough-designed scheduling policy to assign and organize detailed packet transmissions for real-time big data is desired. This paper takes video delivery as a case study, which dominates the current real-time traffic and can be easily extended to other scenarios. We propose a novel scheduling policy, which assigns a proper number of video requests to servers and allocates bandwidth to these requests in a relatively small time scale. It helps with serving more users without compromising user experience of the current ones. We also prove that the scheduling policy has a guaranteed performance on the total number of served requests. Finally, the simulation results demonstrate that our scheduling policy outperforms other state-of-art methods significantly. [ABSTRACT FROM PUBLISHER]

HDR video on mobile devices is in its infancy and there are no solutions yet that can achieve full HDR video reproduction due to computational power limitations. In this paper we present a novel and versatile solution that allows the delivery of HDR video on mobile devices by taking into account contextual information and retro-compatibility for devices that do not have the computational power to decode HDR video. The proposed solution also enables the remote transmission of HDR video to mobile devices in real-time. This context-aware HDR video distribution solution for mobile devices is evaluated and discussed by considering the impact of HDR videos over conventional low dynamic range videos on mobile devices as well as the challenge of playing HDR videos directly locally or remotely. [ABSTRACT FROM AUTHOR]

In this paper, we provide necessary and sufficient conditifor the quality-fair delivery of multimedia contents to mobile users. We control the encoding rates and the transmission rates of the video streams, delivered through a limited capacity channel. This problem is cast into a problem of consensus among identical discrete-time linear systems, connected through a network with fixed and fully connected topology. The information exchanged over the communication network is the measure of the quality of the encoded videos. Based on a consensus result for identical linear systems, we reduce the problem of designing the proportional and integral gains of the encoding rate and transmission rate controllers to a linear static output feedback. We propose an iterative design technique based on linear matrix inequalities to solve the corresponding nonconvex problem, thereby providing a constructive optimality-based approach to the proportional and integral gains tuning problem. We demonstrate the effectiveness of our method in simulations, where we compare it with pre-existing approaches. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Video delivery in Vehicular Ad-hoc NETworks has a great number of applications. However, multimedia streaming over this kind of networks is a very challenging issue because (a) it is one of the most resource-demanding applications; (b) it requires high bandwidth communication channels; (c) it shows moderate to high node mobility patterns and (d) it is common to find high communication interference levels that derive in moderate to high loss rates. In this work, we present a simulation framework based on OMNeT++ network simulator, Veins framework, and the SUMO mobility traffic simulator that aims to study, evaluate, and also design new techniques to improve video delivery over Vehicular Ad-hoc NETworks. Using the proposed simulation framework we will study different coding options, available at the HEVC video encoder, that will help to improve the perceived video quality in this kind of networks. The experimental results show that packet losses significantly reduce video quality when low interference levels are found in an urban scenario. By using different INTRA refresh options combined with appropriate tile coding, we will improve the resilience of HEVC video delivery services in VANET urban scenarios.

On-demand video streaming is becoming a killer application for wireless networks. Recent information-theoretic results have shown that a combination of caching on the users’ devices and device-to-device (D2D) communications yields throughput scalability for very dense networks, which represent critical bottlenecks for conventional cellular and wireless local area network (WLAN) technologies. In this paper, we consider the implementation of such caching D2D systems where each device pre-caches a subset of video files from a library, and users requesting a file that is not already in their library obtain it from neighboring devices through D2D communication. We develop centralized and distributed algorithms for the delivery phase, encompassing a link scheduling and a streaming component. The centralized scheduling is based on the max-weighted independent set (MWIS) principle and uses message-passing to determine max-weight independent sets. The distributed scheduling is based on a variant of the FlashLinQ link scheduling algorithm, enhanced by introducing video-streaming specific weights. In both cases, the streaming component is based on a quality-aware stochastic optimization approach, reminiscent of current Dynamic Adaptive Streaming over HTTP (DASH) technology, for which users sequentially request video “chunks” by choosing adaptively their quality level. The streaming and the scheduling components are coupled by the length of the users’ request queues. Through extensive system simulation, the proposed approaches are shown to provide sizeable gains with respect to baseline schemes formed by the concatenation of off-the-shelf FlashLinQ with proportional fair link scheduling and DASH at the application layer. [ABSTRACT FROM PUBLISHER]