The Content Delivery Networks (CDNs) in a multi-CDN delivery have variable requirements and capabilities. Multi-CDN communication over content steering server aims to address this problem in real-time by allowing a CDN to manage its traffic load based on internal metrics via communication with other CDNs over the content steering server while aligning with the standard. The communication process involves operation requests, acceptances, rejections, or negotiations for client offloading/onloading. The paper implements this approach and utilizes power efficiency as the internal CDN metric. We present the implementation details and the possible lifecycles of a communication process. Future work includes the comprehensive evaluation and introduction of learning-based approaches by predicting future traffic load and initiating communication process before the internal metrics catastrophe.

The emerging technologies of Virtual Reality (VR) and 360$^\circ $video introduce new challenges for state-of-the-art video communication systems. Enormous data volume and spatial user navigation are unique characteristics of 360$^\circ$videos that necessitate a space-time effective allocation of the available network streaming bandwidth over the 360$^\circ$video content to maximize the Quality of Experience (QoE) delivered to the user. Towards this objective, we investigate a framework for viewport-driven rate-distortion optimized 360$^\circ$video streaming that integrates the user view navigation patterns and the spatiotemporal rate-distortion characteristics of the 360$^\circ$video content to maximize the delivered user viewport video quality, for the given network/system resources. The framework comprises a methodology for assigning dynamic navigation likelihoods over the 360$^\circ$video spatiotemporal panorama, induced by the user navigation patterns, an analysis and characterization of the 360$^\circ$video panorama's spatiotemporal rate-distortion characteristics that leverage preprocessed spatial tilling of the content, and an optimization problem formulation and solution that capture and aim to maximize the delivered expected viewport video quality, given a user's navigation patterns, the 360$^\circ$video encoding/streaming decisions, and the available system/network resources. We formulate a Markov model to capture the navigation patterns of a user over the 360$^\circ$video panorama and simultaneously extend our actual navigation datasets by synthesizing additional realistic navigation data. Moreover, we investigate the impact of using two different tile sizes for equirectangular tiling of the 360$^\circ$video panorama. Our experimental results demonstrate the advantages of our framework over the conventional approach of streaming a monolithic uniformly-encoded 360$^\circ$video and a state-of-the-art navigation-speed based reference method. Considerable average and instantaneous viewport video quality gains of up to 5 dB are demonstrated in the case of five popular 4 K 360$^\circ$videos. In addition, we explore the impact of two different popular 360$^\circ$video quality metrics applied to evaluate the streaming performance of our system framework and the two reference methods. Finally, we demonstrate that by exploiting the unequal rate-distortion characteristics of the different spatial sectors of the 360$^\circ$video panorama, we can enable spatially more uniform and temporally higher 360$^\circ$video viewport quality delivered to the user, relative to monolithic streaming.

Heterogeneous spatial quality in 360-degree videos is characterized by regions of high and low quality appearing in the same video chunk. Encoding quality-varying omnidirectional videos enables bandwidth waste optimization without compromising the quality inside the client's viewport. Heterogeneous spatial quality has been implemented using either the concept of tiling or Facebook's offset projection. In this paper, we study the preparation of heterogeneous 360-degree videos with three main contributions. We propose two novel approaches that prepare heterogeneous quality versions of a 360-degree video based on two video decomposition techniques: the Gaussian pyramid and the Laplace pyramid. We also introduce a spherical filter for omnidirectional videos which allows to further reduce the video bit-rate by exploring the spherical properties of the 360-degree videos. We compare the performance of the proposed three methods to tiling and the offset projection in representative scenarios of heterogeneous spatial quality in 360-degree videos and highlight the key trade-off between bit-rate and video quality to consider when implementing these approaches.

The softwarization and virtualization based Network Slicing (NS) technology provides the momentum for integrating the Information-Centric Networking (ICN) systems into the 5G infrastructure, such that ICN can be virtualized as a NS to co-exist with other IP-based vertical services slices. The implementation of the ICN network slice (ICN-NS) is essentially a 5G end-to-end (E2E) NSs embedding problem, which is normally solved by embedding the given virtual network (VN) of the slice instance. However, determining the details of the VN is non-trivial and largely ignored in the literature. In this work, we formulate the ICN network slices determination and embedding (ICN-NS-DE) problem through an Integer Linear Program (ILP) formulation, such that the ICN-NS determination and embedding problems are jointly solved for a hierarchical ICN system without requiring a-priori knowledge on the VN's topology and resource provisioning information. Due to the complexity of the model, we design an heuristic algorithm for solving the problem in practical large scale network. Finally, we demonstrate the performance of the ICN-NS-DE model and the algorithm through intensive simulations.

The growing popularity of applications based on 3D rendering, such as visual effects, gaming, augmented and virtual reality, calls for the development of new solutions for the delivery of 3D objects, in particular textures. The format of texture images, which capture the characteristics of materials, has to address two constraints. First, the delivery on the Internet imposes a reduction of the image size. Second, because of memory limitations, the processing by the rendering engine is done in the GPU by extracting small areas of the image only. The format of texture images should thus enable the random-access feature for independent processing of small blocks of the images, called texels, which negatively affects the texture compression performance and, therefore, the network delivery.

Today's network operators strive to create self-healing cellular networks that have a fully automated troubleshooting management process. To this end, the network monitoring system should be capable of detecting issues, diagnosing them, and triggering the adequate recovery action. In this paper, we propose an unsupervised solution to diagnose the root causes of network issues. As monitoring systems collect a large number of logs from the different devices in their networks, it is possible to determine which connections resulted in a poor user experience and apply a failed/successful label. Our solution, Automatic Root Cause Diagnosis (ARCD), analyzes labeled connection logs to identify the major contributors to the network inefficiency (e.g., a faulty core device) as well as the incompatibilities between different elements (e.g., make and model of a phone not being able to access a service). We evaluate the effectiveness of our solution by using logs from three different real cellular networks. In each case, ARCD was able to identify the major contributors and the most widespread incompatibilities. In the three cases, the precision (detection accuracy) and the recall (detection rate) are higher than 90%.

Augmented Reality (AR) superimposes digital content on top of the real world, to enhance it and provide a new generation of media experiences. To provide a realistic AR experience, objects in the scene should be delivered with both high photorealism and low latency. Current AR experiences are mostly delivered with a download-and-play strategy, where the whole scene is considered a monolithic entity for delivery. This approach results in high start-up latencies and therefore a poor user experience. A similar problem in the video domain has already been tackled with the HTTP Adaptive Streaming (HAS) principle, where the video is split into segments, and a rate adaptation heuristic dynamically adapts the video quality based on the available network resources. In this paper, we apply the adaptive streaming principle from the video to the AR domain, and propose a streaming framework for AR applications. In our proposed framework, the AR objects are available at different Levels-Of-Detail (LODs) and can be streamed independently from each other. An LOD adaptation heuristic is in charge of dynamically deciding what object should be fetched from the server and at what LOD level. Our proposed heuristic prioritizes content that is more likely to be viewed by the user and selects the best LOD to maximize the object's perceived visual quality. Moreover, the adaptation takes into account the available bandwidth resources to ensure a timely delivery of the AR objects. Experiments carried out over the Internet using an AR streaming prototype developed on an iOS device allow us to show the gains brought by the proposed framework. Particularly, our approach can decrease start-up latency up to 90% with respect to a download-and-play baseline, and decrease the amount of data needed to deliver the AR experience up to 79%, without sacrificing on the visual quality of the AR objects.

This artifact includes source code, scripts and datasets required to reproduce the experimental figures in the evaluation of the MM'18 paper, which is entitled "MiniView Layout for Bandwidth-Efficient 360-Degree Video". The artifact reports the comparison results among the standard cube layout (CUBE), the equi-angular layout (EAC), and the MiniView layout (MVL) in terms of compressed video size, visual quality of views and decoding and rendering time.

Delivering rich, high quality 3D scenes over the internet is challenged by the size of the 3D objects in terms of geometry and textures. This paper proposes a new method for the delivery of textures, which are encoded and delivered as a video sequence, rather than independently. Implemented on the existing video delivery infrastructure, our method provides a fine-grained control on the quality of the resulting video sequence.

Planet-scale Augmented Reality (AR) and photorealistic Virtual Reality (VR) are two examples of applications that require the delivery of a rich, wide, and animated 3D scene. We extend this concept to infinitely-scalable and animated 3D scenes and we propose key concepts to launch an open initiative for the delivery of these 3D scenes. We draw on the lessons learned from large-scale implementation of video streaming to design a pull-based mechanism with segmented representations of objects and periodic map updates.

In this article, we propose video delivery schemes insuring around 1s delivery latency with Dynamic Adaptive Streaming over HTTP (DASH), which is a standard version of HTTP Live Streaming (HLS), so as to benefit from the video representation switching between successive video segments. We also propose HTTP/2-based algorithms to apply video frame discarding policies inside a video segment when a selected DASH representation does not match with the available network resources. The current solutions with small buffer suffer from rebuffering events. Rebuffering not only impacts the Quality of Experience (QoE) but also increases the delivery delay between the displayed and the original video streams. In this work, we completely eliminate rebuffering events by developing optimal and practical video frame discarding algorithms to meet the 1s latency constraint. In all our algorithms, we request the video frames individually through HTTP/2 multiple streams, and we selectively drop the least meaningful video frames thanks to HTTP/2 stream resetting feature. Our simulations show that the proposed algorithms eliminate rebuffering while insuring an acceptable video quality with at least a Peak Signal to Noise Ratio (PSNR) of 35dB compared to 25dB of the basic First In First Out (FIFO) algorithm. We also quantify and qualify the resulting temporal distortion of the video segments per algorithm. An important number of missing video frames results in a temporal fluidity break known as video jitter . The displayed video looks like a series of snapshots. We show that both the optimal Integer Linear Program (ILP) and practical algorithms decrease the frequency and duration of the jitters. For example, practical algorithms reduce the number of crashed displayed videos (presenting one jitter longer than 1,350ms) with 22% compared to the basic FIFO algorithm. We also show that requesting video frames separately with HTTP/2 slightly increases the overhead from 4.34% to 5.76%.

The growing popularity of virtual and augmented reality communications and 360° video streaming is moving video communication systems into much more dynamic and resource-limited operating settings. The enormous data volume of 360° videos requires an efficient use of network bandwidth to maintain the desired quality of experience for the end user. To this end, we propose a framework for viewport-driven rate-distortion optimized 360° video streaming that integrates the user view navigation pattern and the spatiotemporal rate-distortion characteristics of the 360° video content to maximize the delivered user quality of experience for the given network/system resources. The framework comprises a methodology for constructing dynamic heat maps that capture the likelihood of navigating different spatial segments of a 360° video over time by the user, an analysis and characterization of its spatiotemporal rate-distortion characteristics that leverage preprocessed spatial tilling of the 360° view sphere, and an optimization problem formulation that characterizes the delivered user quality of experience given the user navigation patterns, 360° video encoding decisions, and the available system/network resources. Our experimental results demonstrate the advantages of our framework over the conventional approach of streaming a monolithic uniformly-encoded 360° video and a state-of-the-art reference method. Considerable video quality gains of 4 - 5 dB are demonstrated in the case of two popular 4K 360° videos.

360° video streaming is coming up against two major technical challenges: network resource consumption and Quality of Experience (QoE). Dynamically adapting the content delivery process to the user behavior is a promising approach to ensure both important network resource savings and satisfying experiences. In this paper, we propose to leverage HTTP Adaptive Streaming (HAS), tiled-based 360° video encoding and the HTTP/2 protocol to implement this dynamic content delivery process. The 360° video stream is spatially encoded into tiles and temporally divided into segments. The client executes two viewport predictions for each segment, one before and one during its delivery. Upon every prediction, it decides on a priority and a quality level for each tile of the video segment; tiles overlapping with the predicted viewport get higher priorities and quality levels. Then it exploits the priority and stream termination features of the HTTP/2 protocol to enforce its decisions. We compare our proposed solution with four alternative schemes on a set of 360° video streaming sessions corresponding to various types of videos, user behaviors and network conditions. Our solution provides better performances: a higher quality on the viewport pixels, a lower ratio of unreceived viewport pixels in bandwidth-constrained networks, and a reduction of the bandwidth consumption, up to 12% compared to the alternative schemes exploiting 2 viewport predictions per video segment.

A video with heterogeneous spatial quality is a video where some regions of the frame have a different quality than other regions (for instance, a better quality could mean more pixels and less encoding distortion). Such a quality-variable encoding is a key enabler of Virtual Reality application, with 360-degree videos. So far, the main technique that has been proposed to prepare spatially heterogeneous quality is based on the concept of tiling. More recently, Facebook has implemented another approach: the offset projection where more emphasis is put on a specific direction of the frame. In this paper, we study quality-variable 360-degree videos with two main contributions. First, we provide the theoretical analysis of the offset projection and show the impact of the parameter settings on the video quality. Second, we propose another approach which consists in preparing the 360-degree video from a Gaussian pyramid of downscaled and blurred versions of the video. We perform an evaluation of tiling, offset and Gaussian-based approaches in representative scenarios of heterogeneous spatial quality in 360-degree videos and highlight the main trade-off to consider when implementing these approaches.

Full immersion inside a Virtual Reality (VR) scene requires six Degrees of Freedom (6DoF) applications where the user is allowed to perform translational and rotational movements within the virtual space. The implementation of 6DoF applications is however still an open question. In this paper we study a multi-viewpoint (MVP) 360-degree video streaming system, where a scene is simultaneously captured by multiple omnidirectional video cameras. The user can only switch positions to predefined viewpoints (VPs). We focus on the new challenges that are introduced by adaptive MVP 360-degree video streaming. We introduce several options for video encoding with existing technologies, such as High Efficiency Video Coding (HEVC) and for the implementation of VP switching. We model three video-segment download strategies for an adaptive streaming client into Mixed Integer Linear Programming (MILP) problems: an omniscient download scheduler; one where the client proactively downloads all VPs to guarantee fast VP switch; one where the client reacts to the user's navigation pattern. We recorded a one MVP 360-degree video with three VPs, implemented a mobile MVP 360-degree video player, and recorded the viewing patterns of multiple users navigating the content. We solved the adaptive streaming optimization problems on this video considering the collected navigation traces. The results emphasize the gains obtained by using tiles in terms of objective quality of the delivered content. They also emphasize the importance of performing further study on VP switching prediction to reduce the bandwidth consumption and to measure the impact of VP switching delay on the subjective Quality of Experience (QoE).

In order to track the users who illegally re-stream live video streams, one solution is to embed identified watermark sequences in the video segments to distinguish the users. However, since all types of watermarked segments should be prepared, the existing solutions require an extra cost of bandwidth for delivery (at least multiplying by two the required bandwidth). In this paper, we study how to reduce the inner delivery (traffic) cost of a Content Delivery Network (CDN). We propose a mechanism that reduces the number of watermarked segments that need to be encoded and delivered. We calculate the best- and worst-case traffics for two different cases: multicast and unicast. The results illustrate that even in the worst cases, the traffic with our approach is much lower than without reducing. Moreover, the watermarked sequences can still maintain uniqueness for each user. Experiments based on a real database are carried out, and illustrate that our mechanism significantly reduces traffic with respect to the current CDN practice.

Software defined networking (SDN) and network function virtualization have emerged as a promising solution for elastic, dynamic, and scalable network management. A collection of works have investigated how virtualization and cloud technology can ameliorate the infrastructure management for the fifth generation mobile network. However, employing these techniques in mobile network for live streaming has not received enough attention. By leveraging the development of SDN and virtualization techniques, mobile live streaming service providers can efficiently manage their system to cope with network dynamics incurred by the variation of the environment. Specifically, we dynamically instantiate network entities at appropriate locations in response to the user live streaming demands. These network entities are in charge of transcoding and transmitting the live videos to the mobile end users, which we name as virtual live streaming agent (vLA). In this paper, we investigate the dynamic virtualization and migration of vLAs for mobile live streaming services. Typically, rate adaptive streaming is adopted to improve spectrum efficiency and user quality of experience. We formulate an integer linear program for the optimal vLA placement problem. Furthermore, we design a practical SDN-based live vLA migration process. Then, by designing and integrating migration cost functions, we develop a dynamic vLA placement mechanism for dynamic environments with the consideration of migration cost. Both heuristic and on-line algorithms are designed for the vLA placement and migration problems. Finally, a large scale real trace-based simulation is conducted to demonstrate the performance of our vLA placement and migration algorithms.

While cloud servers provide a tremendous amount of resources for networked video applications, most successful stories of cloud-assisted video applications are presentational video services, such as YouTube and NetFlix. This article surveys the recent advances on delay-sensitive video computations in the cloud, which are crucial to cloud-assisted conversational video services, such as cloud gaming, Virtual Reality (VR), Augmented Reality (AR), and telepresence. Supporting conversational video services with cloud resources is challenging because most cloud servers are far away from the end users while these services incur the following stringent requirements: high bandwidth, short delay, and high heterogeneity. In this article, we cover the literature with a top-down approach: from applications and experience, to architecture and management, and to optimization in and outside of the cloud. We also point out major open challenges, hoping to stimulate more research activities in this emerging and exciting direction.

The delivery and display of 360-degree videos on Head-Mounted Displays (HMDs) presents many technical challenges. 360-degree videos are ultra high resolution spherical videos, which contain an omnidirectional view of the scene. However only a portion of this scene is displayed on the HMD. Moreover, HMD need to respond in 10 ms to head movements, which prevents the server to send only the displayed video part based on client feedback. To reduce the bandwidth waste, while still providing an immersive experience, a viewport-adaptive 360-degree video streaming system is proposed. The server prepares multiple video representations, which differ not only by their bit-rate, but also by the qualities of different scene regions. The client chooses a representation for the next segment such that its bit-rate fits the available throughput and a full quality region matches its viewing. We investigate the impact of various spherical-to-plane projections and quality arrangements on the video quality displayed to the user, showing that the cube map layout offers the best quality for the given bit-rate budget. An evaluation with a dataset of users navigating 360-degree videos demonstrates that segments need to be short enough to enable frequent view switches.

MPEG Dynamic Adaptive Streaming over HTTP (DASH) has become the most used technology of video delivery nowadays. Considering the video segment more important than its location, new internet architecture such as Content Centric Network (CCN) is proposed to enhance DASH streaming. This architecture with its in-network caching salient feature improves Quality of Experience (QoE) from consumer side. It reduces delays and increases throughput by providing the requested video segment from a near point to the end user. However, there are oscillations issues induced by caching with DASH. In this paper, we propose a new Network-Assisted Strategy (NAS) based-on traffic shaping and request prediction with the aim of improving DASH flows investigating new internet architecture CCN.

In a Mobile-CDN, Base Stations (BSs) are equipped with storages for replicating content, and they are allowed to cooperate in replying user requests through backhaul links. In this paper, we investigate the joint optimization problem of content placement and user request redirection for such a BS-based mobile CDN system. Specifically, each BS maintains a transmission queue for replying user requests issued from other BSs. Due to the limited link capacity and the dynamic network environment, the optimization problem should be jointly considered with the transmission queue states. We employ the Stochastic optimization model to minimize the long-term time-average transmission cost under content availability and network stability constraints. By applying the Lyapunov optimization technique, we transform the long-term problem into a set of linear programming (LP) problems, which are solved in each short time duration. Further, we propose a semi-distributed online algorithm to jointly decide content placement and user request redirection. The evaluation confirms that our solution guarantees network stability comparing to the traditional user request redirection scheme.

For a successful and efficient network supervision, an Anomaly Detection System is essential. In this paper, our goal is to develop a simple, practical, and application-domain specific approach to identify anomalies in the input/output data of network probes. Since data are periodic and continuously evolving, it is not possible to use threshold-based approaches. We propose an algorithm based on pattern recognition to help mobile operators detect anomalies in real time. The algorithm is unsupervised and easily configurable with a small number of tuning parameters. After weeks of deployment in a production network monitoring system, we obtain satisfactory results: we detect major anomalies with low error rate.

Downlink data rates can vary significantly in cellular networks, with a potentially non-negligible effect on the user experience. Content providers address this problem by using different representations (e.g., picture resolution, video resolution and rate) of the same content and switch among these based on measurements collected during the connection. If it were possible to know the achievable data rate before the connection establishment, content providers could choose the most appropriate representation from the very beginning. We have conducted a measurement campaign involving 60 users connected to a production network in France, to determine whether it is possible to predict the achievable data rate using measurements collected, before establishing the connection to the content provider, on the operator's network and on the mobile node. We show that it is indeed possible to exploit these measurements to predict, with a reasonable accuracy, the achievable data rate.

We consider a mobile-CDN system where base stations (BSs) are equipped with storage for replicating and distributing content. In such a system, BSs cooperation in replying user requests is a widely adopted mechanism. For such cooperative caching, a key issue is the joint optimization of content placement and request redirection, which has been intensively investigated in the literature. However, optimizing this problem to guarantee delay has not received enough attention. Practically, each BS maintains a transmission queue for replying requests issued from other BSs. We investigated the management of such transmission queues to guarantee the queuing delay. By solving an admission rate determination problem and a typical content placement and request redirection problem, the throughput of the system is optimized with no violation on the queuing delay. Finally, a real trace based evaluation demonstrates the benefits of managing transmission queues in improving the mobile-CDN system performance.

With the decreasing price of Head-Mounted Displays (HMDs), 360-degree videos are becoming popular. The streaming of such videos through the Internet with state of the art streaming architectures requires, to provide high immersion feeling, much more bandwidth than the median user's access bandwidth. To decrease the need for bandwidth consumption while providing high immersion to users, scientists and specialists proposed to prepare and encode 360-degree videos into quality-variable video versions and to implement viewport-adaptive streaming. Quality-variable versions are different versions of the same video with non-uniformly spread quality: there exists some so-called Quality Emphasized Regions (QERs). With viewport-adaptive streaming the client, based on head movement prediction, downloads the video version with the high quality region closer to where the user will watch. In this paper we propose a generic theoretical model to find out the optimal set of quality-variable video versions based on traces of head positions of users watching a 360-degree video. We propose extensions to adapt the model to popular quality-variable version implementations such as tiling and offset projection. We then solve a simplified version of the model with two quality levels and restricted shapes for the QER. With this simplified model, we show that an optimal set of four quality-variable video versions prepared by a streaming server, together with a perfect head movement prediction, allow for 45% bandwidth savings to display video with the same average quality as state of the art solutions or allows an increase of 102% of the displayed quality for the same bandwidth budget.

While Virtual Reality applications are increasingly attracting the attention of developers and business analysts, the behaviour of users watching 360-degree (i.e. omnidirectional) videos has not been thoroughly studied yet. This paper introduces a dataset of head movements of users watching 360-degree videos on a Head-Mounted Display (HMD). The dataset includes data collected from 59 users watching five 70 s-long 360-degree videos on the Razer OSVR HDK2 HMD. The selected videos span a wide range of 360-degree content for which different viewer's involvement, thus navigation patterns, could be expected. We describe the open-source software developed to produce the dataset and present the test material and viewing conditions considered during the data acquisition. Finally, we show some examples of statistics that can be extracted from the collected data, for a content-dependent analysis of users' navigation patterns. The source code of the software used to collect the data has been made publicly available, together with the entire dataset, to enable the community to extend the dataset.

To address the problem of illegal re-streaming of video streams, existing solutions are based on watermarking the legal video to track the leak users who re-stream the stream on illegal platform. However, these solutions do not aim at tracking leaks as fast as possible, nor are adaptive to the number of users. We present a CDN-based adaptive delivery architecture for watermarked streaming. We propose an algorithm to generate unique sequences of watermarks for the legal delivery. This algorithm is adaptive in the number of users and optimal for the time needed to detect the leak. It meets the demand of live video providers who do not know in advance the number of clients for a stream. Our algorithm copes with thousands of new clients per seconds and enables leak detection in less than five minutes with only five watermarks for live video streams watched by one billion of clients.

Content delivery networks (CDNs) have become key telecommunication actors. They contribute to improve significantly the quality of services delivering content to end users. However, their impact on the ecosystem raises concerns about their “fairness,” and therefore the question of their inclusion in the neutrality debates, becomes relevant. This paper aims at analyzing the impact of a revenue-maximizing CDN on some other major actors, namely, the end-users, the network operators, and the content providers, at comparing the outcome with that of a fair behavior, and at providing tools to investigate whether some regulation should be introduced. We present a mathematical model and show that there exists a unique optimal revenue-maximizing policy for a CDN actor, in terms of dimensioning and allocation of its storage capacity, and depending on parameters such as prices for service/transport/storage. Numerical experiments are then performed with both synthetic data and real traces obtained from a major video-on-demand provider. In addition, using the real traces, we compare the revenue-based policy with policies based on several fairness criteria.

Mobile gaming is an emerging concept wherein gamers are using mobile devices, like smartphones and tablets, to play best-seller games. Compared to dedicated gaming boxes or PCs, these devices still fall short of executing newly complex 3D video games with a rich immersion. Three novel solutions, relying on cloud computing infrastructure, namely, computation offloading, cloud gaming, and client-server architecture, will represent the next generation of game engine architecture aiming at improving the gaming experience. The basis of these aforementioned solutions is the distribution of the game code over different devices (including set-top boxes, PCs, and servers). In order to know how the game code should be distributed, advanced knowledge of game engines is required. By consequence, dissecting and analyzing game engine performances will surely help to better understand how to move in these new directions (i.e., distribute game code), which is so far missing in the literature. Aiming at filling this gap, we propose in this article to analyze and evaluate one of the famous engines in the market, that is, “Unity 3D.” We begin by detailing the architecture and the game logic of game engines. Then, we propose a test-bed to evaluate the CPU and GPU consumption per frame and per module for nine representative games on three platforms, namely, a stand-alone computer, embedded systems, and web players. Based on the obtained results and observations, we build a valued graph of each module, composing the Unity 3D architecture, which reflects the internal flow and CPU consumption. Finally, we made a comparison in terms of CPU consumption between these architectures.

End-users in online video services are sensitive to the overall quality of the video at screen, but also, and more importantly, to other factors, including the latency between the video generation and the playback for live videos. If the improvement of the quality of the perceived video has been well investigated by the multimedia community, the impact of delay, latency, and re-buffering has not received a significant attention. Adaptive streaming technologies, which have been widely adopted in the recent years, contribute to this lack of consideration since vendors usually recommend the introduction of significant extra-latency. In this paper, we address the problem of low-latency live video streaming. We propose an implementation of multi-path video delivery at the applicative level, which exploits the information provided in the latest version of the video delivery standards. We present an implementation of a video player, which takes advantage of multi-path networking to enable video playback using TCP with a latency below 100 ms. By initiating the multi-path delivery from the client side, our mechanism is compatible with current network equipments and does not require any change neither at the server nor in the middleboxes.

Content Delivery Network (CDN) services largely affect the delivery quality perceived by users. While those services were initially offered by independent entities, some large ISP now develop their own CDN activities to control costs and delivery quality. But this new activity is also a new source of revenues for those vertically integrated ISP-CDNs, which can sell those services to content providers. In this paper, we investigate the impact of having an ISP and a vertically-integrated CDN, on the main actors of the ecosystem (users, competing ISPs). Our approach is based on an economic model of revenues and costs, and a multilevel game-theoretic formulation of the interactions among actors. Our model incorporates the possibility for the vertically-integrated ISP to partially offer CDN services to competitors in order to optimize the trade-off between CDN revenue (if fully offered) and competitive advantage on subscriptions at the ISP level (if not offered to competitors). Our results highlight two counterintuitive phenomena: an ISP may prefer an independent CDN over controlling (integrating) a CDN, and from the user point of view vertical integration is preferable to an independent CDN or a no-CDN configuration. Hence, a regulator may want to elicit such CDN-ISP vertical integrations rather than prevent them.

Transport protocols that can exploit multiple paths, especially MPTCP, do not match the requirements of video streaming: high average transmission delay, too strict reliability, and frequent head-of-line phenomenons resulting in abrupt throughput drops. In this paper, we address this mismatch by introducing a cross-layer scheduler, which leverages information from both application and transport layers to re-order the transmission of data and prioritize the most significant parts of the video. Our objective is to maximize the amount of video data that is received in time at the client. We show that current technologies enable the implementation of this cross-layer scheduler without much overhead. We then demonstrate the validity of our approach by studying the performance of an optimal cross-layer scheduler. The gap between the performance of the traditional scheduler versus the optimal scheduler justifies our motivation to implement a cross-layer scheduler in practice. We propose one implementation with basic cross-layer awareness. To evaluate the performance of our proposal, we aggregate a dataset of real MPTCP sessions and we use video stream encoded with HEVC. Our results show that our cross-layer proposal outperforms the traditional scheduler. Viewers not only benefit from the inherent advantages of using MPTCP (such as a better resilience to path failure) but also get a better QoE compared to the traditional scheduler.

The development of Network Function Virtualization (NFV) and Software Defined Networks (SDN) standards is an opportunity for Mobile Network Operators (MNOs) to deploy Content Delivery Network (CDN) functionalities into the mobile network edge, such as Base Stations (BSs). In this paper, we investigated the content placement problem for the BS-based wireless CDN system. We call the storage resources implemented on BSs as storage helpers. Due to the limited helper storage capacity and the limited user population served per BS, helpers exhibit low hit ratio comparing to traditional CDN edge servers serving a wide area. Then, cooperation is a suitable means to enhance the performance of the wireless CDN system. We propose that BSs close to each other cooperate in replicating content and replying user requests. We formulate the optimum content placement problem to minimize the traffic pressure on mobile network gateways, and show the problem complexity is NP-Hard. We then transform the problem into a multiple-Maximum Weighted Independent Set problem, and propose a heuristic algorithm. The evaluation shows that the hit ratio is improved by our algorithm comparing to the traditional Least Frequently Used (LFU) policy without cooperation.

We investigate the impact of decisions made by a CDN willing to maximize its revenue through the management of cache servers. Based on a model with two network providers, we highlight that revenue-oriented management policies can affect the user-perceived quality of experience, impacting the competition among network access providers in favor of the largest one. Since this contradicts the principle underpinning network neutrality-although not with the technical net neutrality rules-we discuss the necessity to regulate CDN activity.

More and more users are watching online videos produced by non-professional sources (e.g., gamers, teachers of online courses, witnesses of public events) by using an increasingly diverse set of devices to access the videos (e.g., smartphones, tablets, HDTV). Live streaming service providers can combine adaptive streaming technologies and cloud computing to satisfy this demand. In this paper, we study the problem of preparing live video streams for delivery using cloud computing infrastructure, e.g., how many representations to use and the corresponding parameters (resolution and bit-rate). We present an integer linear program (ILP) to maximize the average user quality of experience (QoE) and a heuristic algorithm that can scale to large number of videos and users. We also introduce two new datasets: one characterizing a popular live streaming provider (Twitch) and another characterizing the computing resources needed to transcode a video. They are used to set up realistic test scenarios. We compare the performance of the optimal ILP solution with current industry standards, showing that the latter are sub-optimal. The solution of the ILP also shows the importance of the type of video on the optimal streaming preparation. By taking advantage of this, the proposed heuristic can efficiently satisfy a time varying demand with an almost constant amount of computing resources.

User-Generated live video streaming systems are services that allow anybody to broadcast a video stream over the Internet. These Over-The-Top services have recently gained popularity, in particular with e-sport, and can now be seen as competitors of the traditional cable TV. In this paper, we present a dataset for further works on these systems. This dataset contains data on the two main user-generated live streaming systems: Twitch and the live service of YouTube. We got three months of traces of these services from January to April 2014. Our dataset includes, at every five minutes, the identifier of the online broadcaster, the number of people watching the stream, and various other media information. In this paper, we introduce the dataset and we make a preliminary study to show the size of the dataset and its potentials. We first show that both systems generate a significant traffic with frequent peaks at more than 1 Tbps. Thanks to more than a million unique uploaders, Twitch is in particular able to offer a rich service at anytime. Our second main observation is that the popularity of these channels is more heterogeneous than what have been observed in other services gathering user-generated content.

The delivery of live video channels for services such as twitch.tv leverages the so-called Telco-CDN-Content Delivery Network (CDN) deployed within the Internet Service Provider (ISP) domain. A Telco-CDN can be regarded as an intra-domain overlay network with tight resources and critical deployment constraints. This paper addresses two problems in this context: (1) the construction of the overlays used to deliver the video channels from the entrypoints of the Telco-CDN to the appropriate edge servers; and (2) the allocation of the required resources to these overlays. Since bandwidth is critical for entrypoints and edge servers, our ultimate goal is to deliver as many video channels as possible while minimizing the total bandwidth consumption. To achieve this goal, we propose two approaches: a two-step optimization where the optimal overlays are firstly computed, then an optimal resource allocation based on these pre-computed overlays is performed; and a joint optimization where both optimization problems are simultaneously solved. We also devise fast heuristic algorithms for each of these approaches. The conducted evaluations of these two approaches and algorithms provide useful insights into the management of critical Telco-CDN infrastructures.

Adaptive streaming addresses the increasing and heterogeneous demand of multimedia content over the Internet by offering several encoded versions for each video sequence. Each version (or representation) is characterized by a resolution and a bit rate, and it is aimed at a specific set of users, like TV or mobile phone clients. While most existing works on adaptive streaming deal with effective playout-buffer control strategies on the client side, in this article we take a providers' perspective and propose solutions to improve user satisfaction by optimizing the set of available representations. We formulate an integer linear program that maximizes users' average satisfaction, taking into account network dynamics, type of video content, and user population characteristics. The solution of the optimization is a set of encoding parameters corresponding to the representations set that maximizes user satisfaction. We evaluate this solution by simulating multiple adaptive streaming sessions characterized by realistic network statistics, showing that the proposed solution outperforms commonly used vendor recommendations, in terms of user satisfaction but also in terms of fairness and outage probability. The simulation results show that video content information as well as network constraints and users' statistics play a crucial role in selecting proper encoding parameters to provide fairness among users and to reduce network resource usage. We finally propose a few theoretical guidelines that can be used, in realistic settings, to choose the encoding parameters based on the user characteristics, the network capacity and the type of video content.

Adaptive streaming addresses the increasing and heterogenous demand of multimedia content over the Internet by offering several streams for each video. Each stream has a different resolution and bit rate, aimed at a specific set of users, e.g., TV, mobile phone. While most existing works on adaptive streaming deal with optimal playout-control strategies at the client side, in this paper we concentrate on the providers' side, showing how to improve user satisfaction by optimizing the encoding parameters. We formulate an integer linear program that maximizes users' average satisfaction, taking into account the network characteristics, the type of video content, and the user population. The solution of the optimization is a set of encoding parameters that outperforms commonly used vendor recommendations, in terms of user satisfaction and total delivery cost. Results show that video content information as well as network constraints and users' statistics play a crucial role in selecting proper encoding parameters to provide fairness among users and reduce network usage. By combining patterns common to several representative cases, we propose a few practical guidelines that can be used to choose the encoding parameters based on the user base characteristics, the network capacity and the type of video content.

The growth of Internet video traffic imposes a severe capacity problem in today's Content Delivery Network (CDN). Rate-adaptive streaming technologies, such as the Dynamic Adaptive Streaming over HTTP (DASH) standard, reinforces this problem in the core CDN infrastructure since delivering one video means delivering multiple representations for an aggregated bit-rate that is commonly over 10 Mbps. In this paper, we explore better trade-offs between CDN infrastructure cost and Quality of Experience (QoE) of the end-users for live broadcast video streaming applications. We consider in particular underprovisioned CDN networks, our goal being to maximize the QoE for the population of heterogeneous end-users despite the lack of resources in the intermediate CDN equipments. We show that previous theoretical models based on elastic bit-rates do not fit for this context. We propose a user-centric discretized streaming model where the satisfaction of end-users is related to the context and where a stream has to be either delivered in its entirety, or not delivered at all. We first formulate an Integer Linear Program (ILP) that achieves the optimal delivery through a multi-tree delivery overlay. The evaluation of the ILP shows the benefits of this model. We then design a practical system by revisiting the three main algorithms implemented in CDN: user-to-server assignment, content placement and content delivery. At last, we use a realistic trace-driven large-scale simulator to study the performances of our system. In particular, we show that the population of users is reasonably well served (three quarters of the population do not experience degradation) even when the CDN infrastructure experiences a severe underprovisioning (less than half of the required infrastructure).

A Telco-CDN can be regarded as an intra-domain overlay network with tight resources and critical deployment constraints. This paper addresses two problems in this context: (1) the construction of the overlays used to deliver the video channels from the entrypoints of the Telco-CDN to the appropriate edge servers; and (2) the allocation of the required resources to these overlays. Our ultimate goal is to maximize the number of delivered channels while preserving network resources. Two classes of heuristic algorithms, namely two-step optimization and joint-optimization, are proposed to solve these problems. The conducted evaluations confirm the efficiency of the joint-optimization approach.

Content Delivery Networks (CDNs) are confronted with a sharp increase in traffic related to live video (channel) streaming. Previous theoretical models that deal with streaming capacity problems do not capture the emerging reality faced by today's CDNs. In particular, a modern CDN has to deliver a large set of independent non-divisible data streams, which need to be either delivered in whole, or not delivered at all. This constraint is not addressed in previous works. In this paper we identify a new, discretized streaming model for live video delivery in CDNs. For this model we formulate a general optimization problem and show that it is NP-complete. Then we study a practical scenario that occurs in real CDNs. We present a fast, easy to implement, and near-optimal algorithm with performance approximation ratios that are negligible for large network. To our knowledge, these are the first results for the discretized streaming model, and have both practical and theoretical importance in a topic of growing criticality.

The exploding HD video streaming traffic calls for deploying content servers deeper inside network operators' infrastructures. Telco-CDN are new content distribution services that are managed by Internet Service Providers (ISP). Since the network operator controls both the infrastructure and the content delivery overlay, it is in a position to engineer telco-CDN so that networking resources are optimally utilized. In this paper, we show the following two findings: 1. it is possible to implement an efficient algorithm for the placement of video chunks into a telco-CDN. We present an algorithm, which is based on a genetic algorithm implemented on the MapReduce framework. We show that, for a national VoD service, computing a quasi-optimal placement is possible. 2. such push strategy makes sense because it allows to actually take into account fine-grain traffic management strategies on the underlying infrastructure. Our proposal re-opens the debate about the relevance of such "push" approach (where the manager of telco-CDN proactively pushes video content into servers) versus the traditional caching approach (where the content is pulled to the servers from requests of clients). Our proposal of a quasi-optimal tracker enables fair comparisons between both approaches for most traffic engineering policies. We illustrate the interest of our proposal in the context of a major European Telco-CDN with real traces from a popular Video-on-Demand (VoD) service. Our experimental results show that, given a perfect algorithm for predicting user preferences, our placement algorithm is able to keep aligned with LRU caching in terms of the traditional hit-ratio, but the workload on some troubled links (e.g., over-used links) in a push-based strategy is significantly alleviated.

Recent works on Information Centric Networking enable the exploitation of the caching resources in the new generation of routers (Content Routers or CR). So far, only a basic Least Recently Used (LRU) strategy implemented on every CR has been proposed. More generally, the research community lacks methods for analyzing and evaluating caching policies (other than LRU) in generic multi-cache topologies. In this paper, we provide a model that approximates the hit-ratios of any multi-cache topology for the Least Recently/Frequently Used (LRFU) caching policies, which consist of a spectrum of policies based on a trade-off between recency and frequency. We also present a way to approximate the performances of the network of caches when the input traffic changes. The approximation results can be used to decide suitable policy for CR at different positions in the network topology. With appropriate policy for each single CR, we are able to improve the performance of the whole in-network caching system.

Content-Centric Network is a novel Internet design that investigates the shifting of the Internet usage from browsing to content dissemination. This new Internet architecture proposal can bring many benefits and it has attracted many research works. When we study on these research, we found that one of the important components, the Pending Interest Table (PIT), did not get much attention. Since that in CCN the networking behaviours are no longer based on endpoints location but rather on every piece of the content itself, and the PIT is involved in both the forwarding processes upstream and downstream, in case of large amount of requests, the table size is a big issue, that leads to a large required memory space for implementing such a CCN node. In this paper, we propose a distributed PIT table, named DiPIT, where a part of the PIT is on every interface. Our approach relies on Bloom Filters in order to reduce the necessary memory space for implementing the PIT, completed with a central Bloom Filter for limiting the false positives, generated by the individual Bloom Filters. The evaluations we have performed highlight that our DiPIT approach can significantly reduce the memory space (up to 63%) in the CCN node and support a higher incoming packet throughput, compared to the hash table technology, which is largely implemented in current routers.

We propose a peer-to-peer system for streaming user-generated live video. Peers are arranged in levels so that video is delivered at about the same time to all peers in the same level, and peers in a higher level watch the video before those in a lower level. We encode the video bit stream with rate less codes and use trees to transmit the encoded symbols. Trees are constructed to minimize the transmission rate for the source while maximizing the number of served peers and guaranteeing on-time delivery and reliability at the peers. We formulate this objective as a height bounded spanning forest problem with nodal capacity constraint and compute a solution using a heuristic polynomial-time algorithm. We conduct ns-2 simulations to study the trade-off between used bandwidth and video quality for various packet loss rates and link latencies.

Large-scale live streaming systems can experience bottlenecks within the infrastructure of the underlying Content Delivery Network. In particular, the "equipment bottleneck" occurs when the fan-out of a machine does not enable the concurrent transmission of a stream to multiple other equipments. In this paper, we aim to deliver a live stream to a set of destination nodes with minimum throughput at the source and limited increase of the streaming delay. We leverage on rateless codes and cooperation among destination nodes. With rateless codes, a node is able to decode a video block of k information symbols after receiving slightly more than k encoded symbols. To deliver the encoded symbols, we use multiple trees where inner nodes forward all received symbols. Our goal is to build a diffusion forest that minimizes the transmission rate at the source while guaranteeing on-time delivery and reliability at the nodes. When the network is assumed to be lossless and the constraint on delivery delay is relaxed, we give an algorithm that computes a diffusion forest resulting in the minimum source transmission rate. We also propose an effective heuristic algorithm for the general case where packet loss occurs and the delivery delay is bounded. Simulation results for realistic settings show that with our solution the source requires only slightly more than the video bit rate to reliably feed all nodes.

One of the most attractive features of Massively MuItiplayer Online Games (MMOGs) is the possibility for users to interact with a large number of other users in a variety of collaborative and competitive situations. Garners within an MMOG typically become members of active communities with mutual interests, shared adventures, and common objectives. This demonstration presents a peer-to-peer live video system that enables MMOG players to stream screen-captured video of their game. Players can use the system to show their skills, share experience with friends, or coordinate missions in strategy games.

Recent works on Content-Centric-Networking (CCN) enable the exploitation of the caching resources of the new generation of routers (Content Routers or CR). So far, only a basic Least Recently Used (LRU) strategy implemented on every CRs has been proposed. In this paper, we introduce a cooperative caching strategy that has been designed for the treatment of large video streams with on-demand access. This caching strategy addresses the need of Internet Service Provider by halving the cross-domain traffic. We present a first algorithm, which is a core element of this strategy, then we show the changes that have to be brought to the CCN protocol in order to implement this strategy.

One of the most attractive feature of IPTV is the so-called catch-up TV. It allows viewers to watch their favorite TV programs within an expanded time window. In this paper, we emphasize the challenging characteristics of catch-up TV systems that prevent known delivery systems to be used. Then, we formulate the catch-up TV as a multiple-interval graph. Based upon this modeling, we further present a Peer-Assisted Catch-Up Streaming system, namely PACUS, where a set of end users' computers assists the server for the content delivery. We show in particular how the PACUS tracker server can be efficiently implemented for catch-up TV. We demonstrate the benefits of PACUS by simulations. We especially highlight that PACUS reduces the traffic at the server side with the advantages of lightweight and self-adaptive unstructured peer-to-peer systems. Furthermore, comparing four kinds of neighborhood selection strategies implemented on the PACUS server, the network-friendly strategy is strongly recommended to efficiently alleviate the server burden, and reduce significantly the overall network traffic generated by the catch-up TV as well.

In live streaming systems (IPTV, life-stream services, etc.), an attractive feature consists in allowing users to access past portions of the stream. This is called a time-shifted streaming system. We address in this paper the design of a large-scale delivery system for a time-shifted streaming application. We highlight the challenging characteristics of time-shifted applications that prevent known delivery systems to be used. Then, we describe the turntable structure, the first structure that has been specifically designed to cope with the properties of time-shifted systems. A set of preliminary simulations confirm the interest for this structure.

On-demand video delivery with a content delivery network (CDN) solely based on set-top-boxes has been introduced. In previous works, videos are separated into pieces and randomly stored in boxes, requests from clients being redirected to the nearest boxes. However, random strategy may lead to some costly and inefficient allocation. For example, a video part could be placed much further than other parts of the same video, while two close boxes host the same part. This paper aims to reduce downloading cost by exploiting the network location of boxes when allocating the videos. We show that optimizing the allocation is NP-hard. In this paper, in order to analyze the impact of different content allocation methods on cross-domain traffic, we compare an existing approximate algorithm and two simple heuristics with random allocation. Simulations with realistic network settings demonstrate that inter-AS traffic can be efficiently reduced, when knowledge on network topology is integrated in the allocation algorithm, such as links and relationships among autonomous systems.

Peer-to-Peer (P2P) file sharing applications use multiple TCP connections between peers to transfer data. The aggressiveness and robustness of P2P technology remarkably improve transfer efficiency and network bandwidth utilization. However, while the network bottleneck link is congested, P2P applications tend to unfairly steal bandwidth from other traditional Internet applications (Client/Server mode), which deteriorates the performance of traditional Internet applications. The paper proposes a friendlyP2P system with new application-level approaches for congestion detection and avoidance to keep fairness between P2P traffic and traditional Internet traffic. friendlyP2P, which is friendly to ISPs, namely to Internet networks and traditional Internet traffic, detects network congestion via throughput measurements and alleviates network congestion by optimization of the number of P2P connections from the viewpoint of P2P users. friendlyP2P system requires neither network node support nor TCP modification, which makes it easy to deploy. Simulation experiments demonstrate that fairness and congestion avoidance can be achieved in presence of congestion, and network bandwidth can be effectively utilized in absence of congestion with friendlyP2P technology.

The match between a peer-to-peer overlay and the physical Internet infrastructure is a constant issue. Time-constrained peer-to-peer applications such as live streaming systems are even more challenging because participating peers have to discover their closest neighbors as quickly as possible. We propose in this paper an approach based on landmarks and a management server. Despite its centralized architecture, this method can scale to a large population of peers. We focus in this paper on challenging this basic idea through intensive simulations, using a large-scale map of the Internet router (IR) layer in a simulator dedicated to peer-to-peer systems. We compare the results obtained by the new method with a random selection and an optimal selection and we show that this proposal makes sense.

The match between a peer-to-peer overlay and the physical Internet infrastructure is a constant issue. Time-constrained peer-to-peer applications such as live streaming systems are even more challenging because participating peers have to discover their closest neighbors as quickly as possible. We propose in this paper an approach based on landmarks and a management server in order to discover, as quickly as possible, its closest neighbors among a large population of peers.

The data transmission capacity of wireless sensor networks is known to be limited by both the hardware and the sink-based communication paradigm. In a multi-sink application scenario, simultaneous queries may generate traffic exceeding the transmission capacity of certain sensor nodes. In this paper, we intend to share the capacity of the sensors among multiple sinks by adjusting their query ranges, so that no sensor gets congested and every sink is able to monitor an area with desired data rate. Specifically, the max-min fairness will be ensured. An analytical model is presented and theoretical results for two-sink case is given. Then a distributed algorithm allowing sinks to approach the optimal query range with local knowledge is developed. Finally, simulation results are shown to illustrate the characteristics of this capacity sharing problem and to validate the effectiveness of our distributed algorithm.

Publish/Subscribe systems provide a useful platform for delivering data (events) from publishers to subscribers in an anonymous fashion in distributed networks. In this paper, we promote a novel design principle for self-. dynamic and reliable content-based publish/subscribe systems and perform a comparative analysis of its probabilistic and deterministic implementations. More specifically, we present a generic content-based publish/subscribe system, called DPS (Dynamic Publish/Subscribe). DPS combines classical content-based filtering with self-. (self-organizing, selfconfiguring, and self-healing) subscription-driven clustering of subscribers. DPS gracefully adapts to failures and changes in the system while achieving scalable events delivery. DPS includes a variety of fault-tolerant deterministic and probabilistic content-based publication/subscription schemes. These schemes are targeted toward scalability, and aim at reducing and distributing the number of messages exchanged. Reliability and scalability of our system are shown through analytical and experimental evaluation.

Caching Internet based services is a potentially important application for MANETs, as it can improve mobile users' perceived quality of service, reduce their energy consumption, and lower their air-time costs. This paper considers the problem of locating cache proxies in MANETs using several search techniques. The paper first examines several existing and a few novel search techniques including flooding, constrained flooding, a novel dynamic variation of probabilistic flooding, and BFS. These are superimposed on a Maximal Independent Set (MIS), a Connected Dominating Set (DS), and a novel adaptation of BFS-tree based overlays, where each of these overlays is maintained in a self stabilizing manner. The paper also includes a comparison of the performance of these search techniques and overlays by extensive simulations.