Dynamic Adaptive Streaming over HTTP (DASH)

The proliferation of mobile video streaming necessitates efficient HTTP Adaptive Streaming (HAS) to optimize video quality and ensure equitable distribution among clients. Client-side ABR often degrades QoE and yields unfair bitrate allocation under mobile variability. We frame this challenge as a multi-objective optimization and propose EQAH, a Lyapunov-based, edge-assisted quality adaptation heuristic. Executed at the edge node, it optimizes video quality and fairness using a novel throughput estimator, Weighted Harmonic Exponential Averaging (WHEA), which improves robustness and accuracy under dynamic conditions. The algorithm runs once per segment in O(1) time (tens of µs) with fresh buffer/throughput inputs, meeting real-time constraints. Evaluated on public 4G LTE and 5G traces, EQAH outperforms existing edge-assisted methods in both QoE and fairness across diverse mobility scenarios.

Dynamic adaptive video streaming over HTTP (DASH) plays a crucial role in delivering video across networks. Traditional adaptive bitrate (ABR) algorithms adjust video segment quality based on network conditions and buffer occupancy. However, these algorithms rely on fixed rules, making it challenging to achieve optimal decisions considering the overall context. In this paper, we propose a novel deep-reinforcement-learning-based approach for DASH streaming, with the primary focus of maintaining consistent perceived video quality throughout the streaming session to enhance user experience. Our approach optimizes quality of experience (QoE) by dynamically controlling the quality distance factor between consecutive video segments. We evaluate our approach through a comprehensive simulation model encompassing diverse wireless network environments and various video sequences. We also conduct a comparative analysis with state-of-the-art methods. The experimental results demonstrate signifi...

Adaptive video streaming systems are expected to provide the best user experience to improve service engagement. To this purpose, the video player implements a controller to dynamically choose the most suitable video representation to be downloaded. It is wellknown that finding one tuning of the controller's parameters which performs satisfactorily in a wide range of scenarios is very challenging. This paper studies the problem of providing users with (near) optimal Quality of Experience (QoE) for Dynamic Adaptive Streaming over HTTP (DASH) systems. We present ERUDITE, a closed-loop system to optimally tune -at run-time -the adaptive streaming controller's parameters to adapt to changing scenario's parameters. The proposed system is based on a Deep Neural Network (DNN) which continuously provides the streaming controller with estimates of optimal parameters based on measured metrics such as bandwidth samples and overall obtained QoE. The DNN is trained using a dataset that we have built by finding, for thousands of scenarios, the optimal adaptive streaming controller's parameters using a Bayesian optimization algorithm. Results, gathered considering a large number of diverse scenarios, show that ERU-DITE is able to provide near optimal performances by reducing impairments due to rebuffering and video level switching. • Information systems → Multimedia streaming; • Computing methodologies → Control methods; • Computer systems organization → Neural networks.

HTTP Adaptive Streaming (HAS) of video content is becoming an undivided part of the Internet and accounts for most of today's network traffic. Video compression technology plays a vital role in efficiently utilizing network channels, but encoding videos into multiple representations with selected encoding parameters is a significant challenge. However, video encoding is a computationally intensive and time-consuming operation that requires high-performance resources provided by on-premise infrastructures or public clouds. In turn, the public clouds, such as Amazon elastic compute cloud (EC2), provide hundreds of computing instances optimized for different purposes and clients' budgets. Thus, there is a need for algorithms and methods for optimized computing instance selection for specific tasks such as video encoding and transcoding operations. Additionally, the encoding speed directly depends on the selected encoding parameters and the complexity characteristics of video content. In this paper, we first benchmarked the video encoding performance of Amazon EC2 spot instances using multiple ×264 codec encoding parameters and video sequences of varying complexity. Then, we proposed a novel fast approach to optimize Amazon EC2 spot instances and minimize video encoding costs. Furthermore, we evaluated how the optimized selection of EC2 spot instances can affect the encoding cost. The results show that our approach, on average, can reduce the encoding costs by at least 15.8% and up to 47.8% when compared to a random selection of EC2 spot instances.

HTTP adaptive video streaming is a widespread and sought-after technology on the Internet that allows clients to dynamically switch between different stream qualities presented in the bitrate ladder to optimize overall received video quality. Currently, there exist several approaches of different complexity for building such a ladder. The simplest method is to use a static bitrate ladder, and the more complex one is to compute a per-title encoding ladder. The main drawback of these approaches is that they do not provide bitrate ladders for scenes with different visual complexity within the video. Moreover, most modern methods require additional computationally-intensive test encodings of the entire video to construct the convex hull, used to calculate the bitrate ladder. This paper proposes a new fast per-scene encoding approach called FAUST based on 1) quick entropy-based scene detection and 2) prediction of optimized bitrate ladder for each scene using an artificial neural network. The results show that our model reduces the mean absolute error to 0.15, the mean square error to 0.08, and the bitrate to 13.5% while increasing the difference in video multimethod assessment fusion to 5.6 points.

Encoding and transcoding videos into multiple codecs and representations is a significant challenge that requires seconds or even days on high-performance computers depending on many technical characteristics, such as video complexity or encoding parameters. Cloud computing offering on-demand computing resources optimized to meet the needs of customers and their budgets is a promising technology for accelerating dynamic transcoding workloads. In this work, we propose OTEC, a novel multi-objective optimization method based on the mixed-integer linear programming model to optimize the computing instance selection for transcoding processes. OTEC determines the type and number of cloud and fog resource instances for video encoding and transcoding tasks with optimized computation cost and time. We evaluated OTEC on AWS EC2 and Exoscale instances for various administrator priorities, the number of encoded video segments, and segment transcoding times. The results show that OTEC can achieve appropriate resource selections and satisfy the administrator's priorities in terms of time and cost minimization.

HTTP adaptive video streaming is a widespread and sought-after technology on the Internet that allows clients to dynamically switch between different stream qualities presented in the bitrate ladder to optimize overall received video quality. Currently, there exist several approaches of different complexity for building such a ladder. The simplest method is to use a static bitrate ladder, and the more complex one is to compute a per-title encoding ladder. The main drawback of these approaches is that they do not provide bitrate ladders for scenes with different visual complexity within the video. Moreover, most modern methods require additional computationally-intensive test encodings of the entire video to construct the convex hull, used to calculate the bitrate ladder. This paper proposes a new fast per-scene encoding approach called FAUST based on 1) quick entropy-based scene detection and 2) prediction of optimized bitrate ladder for each scene using an artificial neural network. The results show that our model reduces the mean absolute error to 0.15, the mean square error to 0.08, and the bitrate to 13.5% while increasing the difference in video multimethod assessment fusion to 5.6 points.

I samarbejde med CloudPotato; at designe et IT-system, der opsamler lokal vejr-data fra sensorer spredt på øgrupper i stillehavet. Opgaven overvejes for bedst udbytte, udefra relevante undersøgelser af muligheder og lignende produkter. Det udviklet produkt består af en opsamler for den analoge data fra klientens side, der optages og uploades på klientens server, analyseres, og vises på en hjemmeside og distribueres til øboernes enheder.

Video streaming currently dominates global Internet traffic. Live streaming broadcasts events in real-time, with very different characteristics compared to video-on-demand (VoD), being more sensitive to variations in delay, jitter, and packet loss. The use of adaptive streaming techniques over HTTP is massively deployed on the Internet, adapting the video quality to instantaneous condition of the network. Dynamic Adaptive Streaming over HTTP (DASH) is the most popular adaptive streaming technology. In DASH, the client probes the network quality and adjusts the quality of requested video segment according to the bandwidth fluctuations. Therefore, DASH is an over-the-top application using unmanaged networks to distribute content in the best possible quality. In order to maintain a seamless playback, VoD applications commonly use a large reception buffer. However, in live streaming, the use of large buffers is not allowed because of the induced delay. Active Queue Management (AQM) arises as an alternative to control the congestion in router's queue, pressing the traffic sources to reduce their transmission rate when it detects incipient congestion. In this article, we evaluate the performance of recent AQM strategies for real-time adaptive video streaming. Furthermore, we propose a new AQM algorithm to improve the user-perceived video quality. The results show that the proposed method achieves better performance than competing AQM algorithms and improves the video quality in terms of average peak signal-to-noise ratio while keeping the fairness among concurrent flows.

Streaming videos over cellular networks is highly challenging. Since cellular data is a relatively scarce resource, many video and network providers offer options for users to exercise control over the amount of data consumed by video streaming. Our study shows that existing data saving practices for Adaptive Bitrate (ABR) videos are suboptimal: they often lead to highly variable video quality and do not make the most effective use of the network bandwidth. We identify underlying causes for this and propose two novel approaches to achieve better tradeoffs between video quality and data usage. The first approach is Chunk-Based Filtering (CBF), which can be retrofitted to any existing ABR scheme. The second approach is QUality-Aware Data-efficient streaming (QUAD), a holistic rate adaptation algorithm that is designed ground up. We implement and integrate our solutions into two video player platforms (dash.js and ExoPlayer), and conduct thorough evaluations over emulated/commercial cellular networks using real videos. Our evaluations demonstrate that compared to the state of the art, the two proposed schemes achieve consistent video quality that is much closer to the user-specified target, lead to far more efficient data usage, and incur lower stalls.

While traditional multimedia applications such as games and videos are still popular, there has been a significant interest in the recent years towards new 3D media such as 3D immersion and Virtual Reality (VR) applications, especially 360 VR videos. 360 VR video is an immersive spherical video where the user can look around during playback. Unfortunately, 360 VR videos are extremely bandwidth intensive, and therefore are difficult to stream at acceptable quality levels. In this paper, we propose an adaptive bandwidth-efficient 360 VR video streaming system using a divide and conquer approach. We propose a dynamic view-aware adaptation technique to tackle the huge bandwidth demands of 360 VR video streaming. We spatially divide the videos into multiple tiles while encoding and packaging, use MPEG-DASH SRD to describe the spatial relationship of tiles in the 360-degree space, and prioritize the tiles in the Field of View (FoV). In order to describe such tiled representations, we extend MPEG-DASH SRD to the 3D space of 360 VR videos. We spatially partition the underlying 3D mesh, and construct an efficient 3D geometry mesh called hexaface sphere to optimally represent a tiled 360 VR video in the 3D space. Our initial evaluation results report up to 72% bandwidth savings on 360 VR video streaming with minor negative quality impacts compared to the baseline scenario when no adaptations is applied.

Abstract DDoS (Distributed Denial of Service) attacks have emerged as one of the major threats in the field of cybersecurity, posing significant challenges for organizations that rely on online services. This type of attack sends a large volume of requests to a target, overwhelming its capacity to respond. One of the various types of DDoS attacks is the HTTP Flood attack. This paper examines the use of the CIC-IDS2017 data set, which contains instances of HTTP Flood attacks for research purposes. The results show that 96.76% accuracy can be achieved in recognizing DDoS attacks using a Convolutional Neural Network (CNN) and the World of Anomaly (WOA) design. The findings indicate that models trained on data from 2017 yield a higher level of identification compared to other research conducted by Fauzia Talpur and another study by Sapna Sadhwani. Further analysis of the attacks shows that there are moments of high volatility, with the potential for significant risks if the available defenses are ineffective against new DDoS attack strategies. This highlights the importance of ongoing research to enhance detection and response mechanisms for DDoS attacks.

Abstract— HTTP adaptive streaming enables dynamic video
quality adjustment without dedicated infrastructure. However,
client-based heuristics often lack network-wide awareness. In this
paper, we propose EQAH (Edge-based Quality Adaptation
Heuristic), leveraging edge computing for enhanced ABR logic
with minimal delay. EQAH uses dynamic buffer management
based on average throughput, ensuring real-time adjustments for
smooth playback, and optimal quality. Simulations show the QoE
improvements of our proposed method compared to the existing
edge-assisted adaptation scheme.

This research work designs and evaluates synchronized second screen interactive digital media using Moving Picture Experts Group Dynamic Adaptive Streaming over HTTP (MPEG-DASH). Second screen technology involves the use of a computing device, such as a smartphone or tablet, to provide an enhanced viewing experience for content on another device. The study design uses a structured format for defining MPEG-DASH inline events to trigger the second screen events. Furthermore, the ability for users to make choices that impact the story was implemented by generating several manifests and enabling the trigger manifest to refresh after each question. By performing a formal subjective user experiment, this study assesses the Quality of Experience (QoE) performance of the prototype. The results were positive and strongly indicate a high QoE for the entire system. A secondary result of our work is enhancing the QoE methodology for the assessment of second screen applications. The implemented system can easily be incorporated in the MPEG-DASH standard, allowing support for using the inline event for second screen triggering.

HTTP Video streaming has become a strong candidate for video transmission on the Internet thanks to the abundance of web infrastructure. With the recent standardization of the new MPEG Dynamic Adaptive Streaming over HTTP (DASH), the flexibility and implantation of adaptive video systems has increased due to the fact that DASH can operate on a conventional web infrastructure. In this paper we propose an estimation and adaptation system for DASH. The proposed adaptive algorithm is based on client buffer threshold and smooth throughput measures (based on the throughput of previous segments). Before DASH, the standard of Scalable Video Coding (SVC) also arose from the idea of adaptation. Both systems (adaptive system based on SVC and the proposed system for DASH) are compared in terms of Video Quality (VQ) metrics. The results show that the proposed system reacts properly to changes in the network capacity, while maintaining a minimum level of segments in the buffer. The user-perceived quality is better than in the SVC-based adaptive system although the generated traffic is higher.

Adaptive streaming based on DASH offers personalized video experience and smooth playback by allowing dynamical adjustments of the video bitrate to the variations of network conditions. This is especially important for current and future Internet video streaming applications, including emerging ones such as virtual reality-based, as adaptive streaming plays a key role in providing high quality viewing experience, especially in limited bandwidth delivery environments. To enable this promising avenue in a 5G context, efforts are made to consider it alongside multicast and edge caching, as part of the next generation communication technology. In this paper, we model the adaptive streaming transmission problem in a mobile scenario as a multi-source multicast multi-rate problem (MMMP) whose linear relaxation is concave. We decompose the problem in terms of clients and propose the distributed delivery algorithm (DDA). The computation complexity, convergence and time-varying adaptation of DDA are theoretically analyzed. Additionally, to further reduce the computation complexity of the solution, a heuristic approximation method (H-DDA) based on the physical meaning of the problem is proposed and it is also shown how H-DDA converges to the optimal value by numerical means. Finally, we conduct a series of simulation tests to demonstrate the superiority of the proposed HDDA in comparison with other state-of-art solutions.

Constructing server-based Web applications requires creating both Web pages and programs that generate Web pages. This requires a knowledge of the Hypertext Markup Language (HTML), the Common Gateway Interface (CGI) protocol, and a programming language, such as C++, Python, or Perl. While this is not a barrier for programmers, it is for non-programmers. This paper describes WebWriter, an integrated system for constructing Web applications that supports the creation of Web pages by non-programmers. WebWriter includes a direct manipulation Web page editor, the WebWriter Editor, which runs in a Web browser as a CGI service, and the WebWriter Page Generator, which creates new pages as an application runs. As in HyperCard, users create a Web application as a stack of pages, where each page can contain output regions that are filled in at runtime by a script. This paper describes the WebWriter system, issues of server-based authoring tools, and some example applications. Introduction The...

Multimedia video streaming, identified as the dominant internet data consumption service, brings forth challenges in consistently delivering optimal video quality. Dynamic Adaptive Streaming over HTTP (DASH), while prevalent, often encounters buffering problems, causing video pauses due to empty video buffers. This study introduces the Adaptive Scalable Video Streaming (ASViS) protocol as a solution. ASViS incorporates scalable video coding, a flow-controlled User Datagram Protocol (UDP), and deadline-based criteria. A model is developed to predict the behavior of ASViS across varying network conditions. Additionally, the effects of diverse parameters on ASViS performance are evaluated. ASViS adjusts data flow similarly to the Transmission Control Protocol (TCP), based on bandwidth availability. Data are designed to be discarded by ASViS according to video frame deadlines, preventing outdated information transmission. Compliance with RFC 8085 ensures the internet is not overwhelmed....

Video clients employ HTTP-based adaptive bitrate (ABR) algorithms to optimize users’ quality of experience (QoE). ABR algorithms adopt video quality based on the network conditions during playback. The existing state-of-the-art ABR algorithms ignore the fact that video streaming services deploy segment durations differently in different services, and HTTP clients offer distinct buffer sizes. The existing ABR algorithms use fixed control laws and are designed with predefined client/server settings. As a result, adaptation algorithms fail to achieve optimal performance across a variety of video client settings and QoE objectives. We propose a buffer- and segment-aware fuzzy-based ABR algorithm that selects video rates for future video segments based on segment duration and the client’s buffer size in addition to throughput and playback buffer level. We demonstrate that the proposed algorithm guarantees high QoE across various video player settings and video content characteristics. Th...

Mobile video traffic accounted for more than half of all mobile data traffic over the past two years. Due to the limited bandwidth, users demand for high-quality video streaming becomes a challenge, which could be addressed by exploiting the emerging diversity of access network and adaptive video streaming. In this paper, a network selection algorithm is proposed for Dynamic Adaptive Streaming over HTTP (DASH), the famous international standard on video streaming, to enhance the received video quality to a "multi-homed user" equipped with multiple interfaces. A Multi-Armed Bandit (MAB) heuristic is proposed for a dynamic selection of the best interface at each step. While the Adaptive Bitrate Rules (ABR) used in DASH allow the video player client to dynamically pick the bit rate level according to the perceived network conditions, at each switching step a quality degradation may occur due to the difference in network conditions of the available interfaces. This paper aims to close this gap by (i) designing a MAB algorithm over DASH for a multihomed user, (ii) evaluating the proposed mechanism through a test-bed implementation, (iii) extending the classic MAB model and (iv) discussing some open issues.

Nowadays, multimedia streaming traffic reaches 71 % from the mobile data traffic over the world and most of the multimedia services use Dynamic adaptive streaming over HTTP (DASH) to adjust video delivery to the dynamic network environment and achieve higher user Quality of Experience (QoE) levels. Moreover, 90 % of the video traffic is consumed by smart devices equipped with multiple network interfaces (Wifi, 3G, and 4G) known as multi-homed devices. In this paper, we propose a new DASH-parallel streaming solution, which exploits the diversity of access network connections to improve video quality over DASH protocol. Experimental Results show that our proposed approach improves the perceived QoE in different network conditions, without requiring further energy expenditure. Index Terms-MPEG-DASH,multi-homing and parallel streaming.

We present Gestu-Wan, an intelligible gesture guidance system designed to support mid-air gesture-based interaction for walk-upand-use displays. Although gesture-based interfaces have become more prevalent, there is currently very little uniformity with regard to gesture sets and the way gestures can be executed. This leads to confusion, bad user experiences and users who rather avoid than engage in interaction using mid-air gesturing. Our approach improves the visibility of gesture-based interfaces and facilitates execution of mid-air gestures without prior training. We compare Gestu-Wan with a static gesture guide, which shows that it can help users with both performing complex gestures as well as understanding how the gesture recognizer works.

The steady demand for performance in Media Content dissemination has forced both Network and Service Providers acting in (real time) content streaming to innovate new techniques and extend existing ones as to improve Quality of Services (QoS). In many situations Content Service Providers (CSP) and Network Providers (NP) operate as distinct entities, making it often difficult for CSPs to acquire detailed network related information. This context introduced the necessity of new emerging technologies, able to resolve this issue as far as it is possible under these restricted conditions. In our paper we address (partially) the mentioned points, by proposing a strategy for dynamic Content Server overload detection and selection, based solely on measurements done at either server location and/or client premises, utilizing a method derived from Financial Technical Analysis.

Single-source HTTP Adaptive Streaming protocols (HAS), such as MPEG-DASH, have become the de-facto solutions to deliver video over the Internet. By avoiding buffer stalling events that are mainly caused by the lack of throughput at client or at server side, HAS protocols increase end-user’s Quality of Experience (QoE). We propose to extend HAS capabilities to a pragmatic DASH-compliant Multiple-Source Streaming solution (MS-Stream) that simultaneously utilizes several servers. MS-Stream offers the opportunity to obtain higher QoE by exploiting expanded bandwidth and link diversity in heterogeneous distributed streaming infrastructures, such as distributed home-gateways or geographically distributed set-top-boxes belonging to Over-The-Top video service providers. This paper exposes a cost-effective two-phase adaptation process with dual (i.e., bitrate and number of sources) adaptation decisioning prior segment request and in-segment download adaptation.

Content/media streaming services are frequently used in the current Internet and this trend is estimated to continue in the next years. Complex architectures, like Content Delivery Networks, have been developed and are currently exploited on a large scale, or novel architectures like Content Oriented Networks are proposed. In parallel, cheaper and light architecture, working in over-the-top (OTT) style above the current IP networks multi-domain infrastructures have been recently considered, as an attractive solution for media distribution. A content streaming OTT-like system is considered here, working on top of a multi-domain IP network. The system integrates functionalities such as content server initial selection based on multi-criteria algorithm and then performs dynamic media adaptation and/or server switchingduring the media session. This paper continues and extends a preliminary work; it presents more deeply the main design concepts, then develops the implementation solutions and presents the functional conformance testing for the Control Plane of the above system. The functional testing is described, performed on a real-life multi-domain testbed as a part of the overall validation campaign.

Over the past few years, adaptive bitrate streaming protocols, such as DASH, have risen to enhance the End-Users' experience towards video consumption over the Internet. Current methods perform a smoother playback (i.e., fewer re-buffering states) trading off with quality fluctuations due to the client-server link state. The proposed work goes a step further and introduces an innovative lightweight streaming solution by taking advantage of bandwidth aggregation over multiple paths using multiple content sources at the same time. This pragmatic evolving approach outperforms the QoE delivered by current DASH-based or P2P-based solutions and leverages the scope of action made available to the media delivery chain actors. The proposed solution has been implemented and is compared to a DASH-based approach used in most existing VoD use-cases through a first set of experiments. Results demonstrate the strong advantages in terms of quality delivered at the End-User's side and buffer occupancy. Discussion on the necessary trade-offs is also tackled.

Adaptive bitrate streaming protocols, such as DASH, have seen extensive interests for their adaptation capabilities to increase consumers' Quality of Experience (QoE) over the Internet, and have become de-facto standards in web video delivery. Compared with traditional single-server approaches, multipleserver streaming offers the opportunity to exploit expanded bandwidth, link diversity, and reliability. In this paper, we expose our solution for multiple-server support to dynamic adaptive streaming applications: Multiple-Source Streaming (MS-Stream). Thanks to its codec agnosticism and DASH-compliance our contribution is a pragmatic and evolving solution for QoE enhancement that can be applied to many streaming architectures (CDNs, Clouds) and is particularly suited for distributed environments such as P2P or Set-Top-Box overlays. In addition, splitting content into multiple independent sub-streams provides the opportunity to achieve easy-to-design bitrate adaptation and server-switching mechanisms. We empirically validate our approach using an extensive collection of network profiles provided by the DASH Industry Forum. Our solution is compared with the full potential of DASH with several servers over several QoE criteria. Results show the QoE gain of using MS-Stream against DASH; an online demonstration is made available.

Real time content delivery is a high popularity service in Internet. In contrast with some complex architectures like Content Delivery Networks, Content Oriented Networks, this paper considers a light architecture, working on top of the current networking technologies. It enhances the (video) content delivery via Internet, based on multi-criteria content server initial selection and then insession media adaptation and/or server handover, all the above in a unified solution. This in-progress work explores different design variants, illustrating the solution flexibility.

Adaptive content streaming is an efficient and cheap solution to achieve a good Quality of Service and Experience for media streaming, having an Over-the –Top light architecture, i.e., working on top of the current IP technologies. The adaptation in the system considered here consists in content server initial optimized selection based on multi-criteria algorithm and then in-session media adaptation (using dynamic adaptive streaming) and/or server switching. In this paper, a first set of results on the implementation and functional validation of the system are presented. It focuses on presenting some of the functional validation scenarios and the tests results of the server selection component.

Appropriate content server and path selection procedures based on different algorithms constitute the first set of actions to be performed in content delivery systems. Multi-criteria optimization algorithms based on user context, network and servers information can be used to enhance the overall efficiency. This paper contains a preliminary work, focused on algorithms and policies for optimized paths and server selection, aiming to finally implement a subsystem in the framework of a content delivery light architecture system.

Adaptive content streaming is frequently used as an efficient and cheap solution to achieve a good quality for media streaming, in systems having light Over-the-Top architectures. The streaming system developed here makes an initial optimized server selection based on multi-criteria algorithms and then insession media adaptation. The focus of this paper is on performance analysis (based on real-life testbed experiments) as part of the overall system validation. Keywords-media content delivery, server and path selection, quality of services, quality of experience, dynamic adaptive streaming, monitoring. I.

Content/media streaming services are frequently used in the current Internet and this trend is estimated to continue in the next years. Complex architectures, like Content Delivery Networks, have been developed and are currently exploited on a large scale, or novel architectures like Content Oriented Networks are proposed. In parallel, cheaper and light architecture, working in over-the-top (OTT) style above the current IP networks multi-domain infrastructures have been recently considered, as an attractive solution for media distribution. A content streaming OTT-like system is considered here, working on top of a multi-domain IP network. The system integrates functionalities such as content server initial selection based on multi-criteria algorithm and then performs dynamic media adaptation and/or server switchingduring the media session. This paper continues and extends a preliminary work; it presents more deeply the main design concepts, then develops the implementation solutions and presents the functional conformance testing for the Control Plane of the above system. The functional testing is described, performed on a real-life multi-domain testbed as a part of the overall validation campaign.

Over the past few years, adaptive bitrate streaming protocols, such as DASH, have risen to enhance the End-Users' experience towards video consumption over the Internet. Current methods perform a smoother playback (i.e., fewer re-buffering states) trading off with quality fluctuations due to the client-server link state. The proposed work goes a step further and introduces an innovative lightweight streaming solution by taking advantage of bandwidth aggregation over multiple paths using multiple content sources at the same time. This pragmatic evolving approach outperforms the QoE delivered by current DASH-based or P2P-based solutions and leverages the scope of action made available to the media delivery chain actors. The proposed solution has been implemented and is compared to a DASH-based approach used in most existing VoD use-cases through a first set of experiments. Results demonstrate the strong advantages in terms of quality delivered at the End-User's side and buffer occupancy. Discussion on the necessary trade-offs is also tackled.

Adaptive bitrate streaming protocols, such as DASH, have seen extensive interests for their adaptation capabilities to increase consumers' Quality of Experience (QoE) over the Internet, and have become de-facto standards in web video delivery. Compared with traditional single-server approaches, multipleserver streaming offers the opportunity to exploit expanded bandwidth, link diversity, and reliability. In this paper, we expose our solution for multiple-server support to dynamic adaptive streaming applications: Multiple-Source Streaming (MS-Stream). Thanks to its codec agnosticism and DASH-compliance our contribution is a pragmatic and evolving solution for QoE enhancement that can be applied to many streaming architectures (CDNs, Clouds) and is particularly suited for distributed environments such as P2P or Set-Top-Box overlays. In addition, splitting content into multiple independent sub-streams provides the opportunity to achieve easy-to-design bitrate adaptation and server-switching mechanisms. We empirically validate our approach using an extensive collection of network profiles provided by the DASH Industry Forum. Our solution is compared with the full potential of DASH with several servers over several QoE criteria. Results show the QoE gain of using MS-Stream against DASH; an online demonstration is made available.

Real time content delivery is a high popularity service in Internet. In contrast with some complex architectures like Content Delivery Networks, Content Oriented Networks, this paper considers a light architecture, working on top of the current networking technologies. It enhances the (video) content delivery via Internet, based on multi-criteria content server initial selection and then insession media adaptation and/or server handover, all the above in a unified solution. This in-progress work explores different design variants, illustrating the solution flexibility.

Content streaming delivery has been recently considered as an attractive solution for media distribution, based on light architectures, working on top of the current Internet Protocol (IP) technologies. Such a system is considered here, integrating functionalities such as content server initial selection based on multi-criteria algorithm and then media adaptation (using dynamic adaptive streaming) and/or server switching-during the media session. This work-in-progress contributes to identify the main design concepts for the Control Plane of the architecture and in particular for the monitoring of Quality of Services (QoS) and Quality and Experience (QoE), aiming to support both the server selection and in-session adaptation actions.

Adaptive content streaming is an efficient and cheap solution to achieve a good Quality of Service and Experience for media streaming, having an Over-the –Top light architecture, i.e., working on top of the current IP technologies. The adaptation in the system considered here consists in content server initial optimized selection based on multi-criteria algorithm and then in-session media adaptation (using dynamic adaptive streaming) and/or server switching. In this paper, a first set of results on the implementation and functional validation of the system are presented. It focuses on presenting some of the functional validation scenarios and the tests results of the server selection component.

This paper studies the management system scalability properties of a networked media eco-system. The system offers guaranteed Quality of Services (QoS) multimedia delivery, over multiple domain networks, based on creation of data plane slices named Virtual Content Aware Networks (VCAN). The VCANs are realized under control of a management plane, by centralized per network domain "controllers" - cooperating in order to span the VCANs over multiple IP domains. Scalability is an issue- similar to the multi-controller problem, in emerging Software Defined Networking (SDN) technologies. The management system architecture considered in this paper has been previously defined. This work provides a simulation model and results concerning the multi-controller communications. It is shown that SDN-like control approach is conveniently feasible in a multi-domain environment.

Content/media streaming services are frequently used in the current Internet and this trend is estimated to continue in the next years. Complex architectures, like Content Delivery Networks, have been developed and are currently exploited on a large scale, or novel architectures like Content Oriented Networks are proposed. In parallel, cheaper and light architecture, working in over-the-top (OTT) style above the current IP networks multi-domain infrastructures have been recently considered, as an attractive solution for media distribution. A content streaming OTT-like system is considered here, working on top of a multi-domain IP network. The system integrates functionalities such as content server initial selection based on multi-criteria algorithm and then performs dynamic media adaptation and/or server switching - during the media session. This paper continues and extends a preliminary work; it presents more deeply the main design concepts, then develops the implementation soluti...

Real time content delivery is a high popularity service in Internet. In contrast with some complex architectures like Content Delivery Networks, Content Oriented Networks, this paper considers a light architecture, working on top of the current networking technologies. It enhances the (video) content delivery via Internet, based on multi-criteria content server initial selection and then insession media adaptation and/or server handover, all the above in a unified solution. This in-progress work explores different design variants, illustrating the solution flexibility.

Single-source HTTP Adaptive Streaming protocols (HAS), such as MPEG-DASH, have become the de-facto solutions to deliver video over the Internet. By avoiding buffer stalling events that are mainly caused by the lack of throughput at client or at server side, HAS protocols increase end-user’s Quality of Experience (QoE). We propose to extend HAS capabilities to a pragmatic DASH-compliant Multiple-Source Streaming solution (MS-Stream) that simultaneously utilizes several servers. MS-Stream offers the opportunity to obtain higher QoE by exploiting expanded bandwidth and link diversity in heterogeneous distributed streaming infrastructures, such as distributed home-gateways or geographically distributed set-top-boxes belonging to Over-The-Top video service providers. This paper exposes a cost-effective two-phase adaptation process with dual (i.e., bitrate and number of sources) adaptation decisioning prior segment request and in-segment download adaptation.

Media content streaming and delivery is nowadays a high popularity service in Internet. Complex architectures like Content Delivery Networks, Content Aware/Oriented Networks have been proposed, where information/content objects are treated as first-class abstractions. As alternative, light architectures, are investigated and implemented, working on top of the current IP networking technologies. In both types of architectures, appropriate content server and path selection constitute the primary set of actions to be performed in the content delivery systems. Such a problem belongs to the general class of multicriteria optimization problem, having (in our case) as input, some information on servers, network and user context. This paper contains an extension of a preliminary work, focused on algorithms and policies for optimized paths and server selection. Simulation study results are presented here, to illustrate the better performance of multi-criteria optimization algorithms versus r...

Appropriate content server and path selection procedures based on different algorithms constitute the first set of actions to be performed in content delivery systems. Multi-criteria optimization algorithms based on user context, network and servers information can be used to enhance the overall efficiency. This paper contains a preliminary work, focused on algorithms and policies for optimized paths and server selection, aiming to finally implement a subsystem in the framework of a content delivery light architecture system.

Adaptive bitrate streaming protocols, such as DASH, have seen extensive interests for their adaptation capabilities to increase consumers' Quality of Experience (QoE) over the Internet, and have become de-facto standards in web video delivery. Compared with traditional single-server approaches, multipleserver streaming offers the opportunity to exploit expanded bandwidth, link diversity, and reliability. In this paper, we expose our solution for multiple-server support to dynamic adaptive streaming applications: Multiple-Source Streaming (MS-Stream). Thanks to its codec agnosticism and DASH-compliance our contribution is a pragmatic and evolving solution for QoE enhancement that can be applied to many streaming architectures (CDNs, Clouds) and is particularly suited for distributed environments such as P2P or Set-Top-Box overlays. In addition, splitting content into multiple independent sub-streams provides the opportunity to achieve easy-to-design bitrate adaptation and server-switching mechanisms. We empirically validate our approach using an extensive collection of network profiles provided by the DASH Industry Forum. Our solution is compared with the full potential of DASH with several servers over several QoE criteria. Results show the QoE gain of using MS-Stream against DASH; an online demonstration is made available.

Over the past few years, adaptive bitrate streaming protocols, such as DASH, have risen to enhance the End-Users' experience towards video consumption over the Internet. Current methods perform a smoother playback (i.e., fewer re-buffering states) trading off with quality fluctuations due to the client-server link state. The proposed work goes a step further and introduces an innovative lightweight streaming solution by taking advantage of bandwidth aggregation over multiple paths using multiple content sources at the same time. This pragmatic evolving approach outperforms the QoE delivered by current DASH-based or P2P-based solutions and leverages the scope of action made available to the media delivery chain actors. The proposed solution has been implemented and is compared to a DASH-based approach used in most existing VoD use-cases through a first set of experiments. Results demonstrate the strong advantages in terms of quality delivered at the End-User's side and buffer occupancy. Discussion on the necessary trade-offs is also tackled.

Adaptive content streaming is frequently used as an efficient and cheap solution to achieve a good quality for media streaming, in systems having light Over-the-Top architectures. The streaming system developed here makes an initial optimized server selection based on multi-criteria algorithms and then insession media adaptation. The focus of this paper is on performance analysis (based on real-life testbed experiments) as part of the overall system validation. Keywords-media content delivery, server and path selection, quality of services, quality of experience, dynamic adaptive streaming, monitoring. I.