As immersive media becomes more accessible, virtual counterparts to real-world experiences such as concerts and conferences have emerged, enabled by volumetric streaming pipelines for virtual reality (VR). User representation is central to these systems, with point clouds widely adopted for realistic avatars due to their balance between quality and performance. However, most systems struggle to scale to larger user counts. While recent work has proposed more scalable architectures, these typically focus on computational optimizations and fail to scale to high user counts due to network bottlenecks. To address this gap, we present an open-source, modular, distributed WebRTC-based volumetric streaming pipeline that employs multiple selective forwarding units (SFUs) to improve latency, throughput, and quality compared to a centralized SFU. Results show that, with 64 users, the distributed setup receives 147% more points with comparable transport latency, and reduces peak latency at lower user counts. Furthermore, leveraging quality adaptation enables stable latency across scales, achieving approximately 25 ms.

Immersive technologies promise to revolutionize communication through enhanced sense of presence and interactivity. To enable interaction, reliable low-latency transport mechanisms are needed to handle the large volumes of data created by complex 3D objects. In this paper, we propose an open-source, codec-independent, selective forwarding unit (SFU) for real-time volumetric video streaming using WebRTC. For evaluation purposes, we provide a reference client implementation by extending VR2Gather, a TCP-based system for immersive communication. We conduct extensive evaluations using both new and existing datasets to compare the performance of WebRTC against TCP-based protocols in an emulated testbed environment. The evaluations demonstrate that WebRTC outperforms other protocols in high-latency scenarios and adapts video quality to user movement 13% and 36% faster than its TCP-based counterparts in networks with 5 ms and 10 ms of network latency, respectively.

Video consumption has become central to modern life, with users seeking more immersive experiences such as virtual conferencing or concerts within virtual reality (VR). While 360° video offers rotational movement, it lacks true positional freedom. Fully immersive formats like light fields and volumetric video enable six degrees-of-freedom (6DoF), allowing both types of freedom. However, their high bandwidth and computational demands make them impractical for low-latency applications. Efforts to address these issues through compression and quality adaptation have improved quality of experience (QoE), but real-time interaction remains limited because of latency. To solve this, we introduce a novel, open-source one-to-many streaming architecture using point cloud-based volumetric video. By compressing point clouds with the Draco codec and transmitting via web real-time communication (WebRTC), we achieve low-latency 6DoF streaming. Content is adapted by employing a multiple description coding (MDC) strategy which combines sampled point cloud descriptions using the estimated bandwidth returned by the Google congestion control (GCC) algorithm. MDC encoding scales more easily to a larger number of users compared to individual encoding. Our proposed solution achieves similar real-time latency for both three and eight clients ( \(163\,\mathrm{m}\mathrm{s}\) and \(166\,\mathrm{m}\mathrm{s}\) ), which is 9% and 19% lower compared to individual encoding. The MDC-based approach, using three workers, achieves similar visual quality compared to a per client encoding solution using five worker threads, and increased quality when the number of clients is greater than 20. Additionally, when compared to an approach with five fixed quality levels, our MDC-based approach scores 13% better in terms of latency, while achieving similar quality.

The demand for high-quality, low-latency video streaming is placing strain on conventional internet infrastructures. This article proposes a hybrid unicast–broadcast video delivery framework designed to address this challenge by integrating advanced 5G broadcast technologies with traditional unicast methods. By offloading popular content to a broadcast network, the approach aims to alleviate congestion and enhance overall streaming efficiency. To ensure reliable video segment delivery over the broadcast network, regardless of the physical layer, we incorporate Packet Recovery (PR) and Forward Error Correction (FEC) mechanisms. Additionally, Temporal Layer Injection (TLI) is employed to further improve video quality while maintaining reduced bandwidth requirements compared to traditional unicast-only approaches. This innovative framework leverages 5G terrestrial broadcasting within Over-the-Top (OTT) streaming environments, enabling seamless delivery of adaptive video content with sub-1-second live latency. Comprehensive experimentation and evaluation through large-scale emulation demonstrate the efficacy of this hybrid approach in meeting the evolving demands of modern multimedia delivery systems. Notably, when broadcasting the top three most commonly watched video streams, 63% of viewers no longer need to request video segments via unicast, as they are efficiently delivered over broadcast channels. This hybrid model offers significant scalability, cost reduction for an ISP, and efficiently delivers content directly to user devices without additional intermediaries, improving viewer experience through low-latency, high-quality streaming.

Current networking solutions are unable to satisfy the low-latency requirements of real-time volumetric video conferencing when faced with heavy congestion scenarios. Traditional congestion controllers use packet loss or the change in round-trip time (RTT) to estimate the bandwidth. Commonly, this method is too slow as congestion has already occurred and the receiving user has already experienced a latency spike. Low latency, low loss and scalable throughput (L4S), recently published as RFC 9330, wants to alleviate this problem by aiming for sub 1 ms queuing delay for low-latency traffic by using accurate explicit congestion notification (AccECN) packet marking to notify applications of early congestion. We propose an L4S-based pipeline for volumetric video delivery, which achieves a more consistent latency under congestion compared to web real-time communication (WebRTC). In addition, L4S bandwidth estimation achieves a 45% faster convergence compared to Google congestion control (GCC) estimation, commonly used in WebRTC. Furthermore, in our detailed evaluation setup the L4S application experiences no packet loss, while the WebRTC-based version suffers from irrecoverable packet loss, resulting in 3% of frames being undecodable.

In response to the growing demand for low-latency video streaming, this paper presents a demonstration of a hybrid unicast-broadcast video delivery system that combines 5G terrestrial broadcasting with over-the-top (OTT) streaming methods. The demonstration features a scalable setup with an interactive dashboard, allowing users to experiment with various configurations and observe key metrics such as bandwidth usage, packet loss, buffer size, and live latency in real-time. Key techniques include Low-Latency DASH (LL-DASH) for HTTP Adaptive Streaming (HAS), packet recovery (PR) and Forward Error Correction (FEC) for reliability, Temporal Layer Injection (TLI) for enhanced quality, and Common Media Application Format (CMAF) with Chunked Transfer Encoding (CTE) for reduced latency. The demonstration shows that this scalable hybrid approach can effectively reduce unicast bandwidth to nearly 0 Mb/s in scenarios without packet loss on the broadcast network, and achieve similar bandwidth reductions in lossy broadcast networks with appropriate Forward Error Correction (FEC) settings, while maintaining a live latency lower than 1 second. These results demonstrate the system's potential for optimizing multimedia delivery, significantly reducing unicast bandwidth while maintaining low-latency streaming.

The production and consumption of video content has become a staple in the current day and age. With the rise of virtual reality (VR), users are now looking for immersive, interactive experiences which combine the classic video applications, such as conferencing or digital concerts, with newer technologies. By going beyond 2D video into a 360 degree experience the first step was made. However, a 360 degree video offers only rotational movement, making interaction with the environment difficult. Fully immersive 3D content formats, such as light fields and volumetric video, aspire to go further by enabling six degrees-of-freedom (6DoF), allowing both rotational and positional freedom. Nevertheless, the adoption of immersive video capturing and rendering methods has been hindered by their substantial bandwidth and computational requirements, rendering them in most cases impractical for low latency applications. Several efforts have been made to alleviate these problems by introducing specialized compression algorithms and by utilizing existing 2D adaptation methods to adapt the quality based on the user's available bandwidth. However, even though these methods improve the quality of experience (QoE) and bandwidth limitations, they still suffer from high latency which makes real-time interaction unfeasible. To address this issue, we present a novel, open source [32], one-to-many streaming architecture using point cloud-based volumetric video. To reduce the bandwidth requirements, we utilize the Draco codec to compress the point clouds before they are transmitted using WebRTC which ensures low latency, enabling the streaming of real-time 6DoF interactive volumetric video. Content is adapted by employing a multiple description coding (MDC) strategy which combines sampled point cloud descriptions based on the estimated bandwidth returned by the Google congestion control (GCC) algorithm. MDC encoding scales more easily to a larger number of users compared to performing individual encoding. Our proposed solution achieves similar real-time latency for both three and nine clients (163 ms and 166 ms), which is 9% and 19% lower compared to individual encoding. The MDC-based approach, using three workers, achieves similar visual quality compared to a per client encoding solution, using five worker threads, and increased quality when the number of clients is greater than 20.

In today's world, the use of video conferencing applications has risen significantly. However, with the introduction of affordable head-mounted displays (HMDs), users are now seeking new immersive and engaging experiences that enhance the 2D video conferencing applications with a third dimension. Immersive video formats such as light fields and volumetric video aim to enhance the experience by allowing for six degrees-of-freedom (6DoF), resulting in users being able to look and walk around in the virtual space. We present a novel, open source, many-to-many streaming architecture using point cloud-based volumetric video. To ensure bitrates that satisfy contemporary networks, the Draco codec encodes the point clouds before they are transmitted using web real-time communication (WebRTC), all while ensuring that the end-to-end latency remains acceptable for real-time communication. A multiple description coding (MDC)-based quality adaptation approach ensures that the pipeline can support a large number of users, each with varying network conditions.

The increasing demand for high-quality video streaming, coupled with the necessity for low-latency delivery, presents significant challenges in today's multimedia landscape. In response to these challenges, this research explores the optimization of adaptive video streaming by integrating 5G terrestrial broadcasting with over-the-top (OTT) streaming methods. A comprehensive integration of forward error correction (FEC), temporal layer injection (TLI), and broadcast techniques enhance the robustness and efficiency of content delivery over broadcast networks and reduce unicast bandwidth to zero in low loss environments. Multiple strategies are compared through an extensive emulation setup for reducing latency in the end-to-end video delivery chain to sub 3-second live latency, demonstrating the effectiveness of a hybrid unicast-broadcast approach in achieving low-latency while maintaining high-quality video streaming performance with significantly reduced bandwidth. For 62.99% of viewers, unicast bandwidth can be reduced to as low as zero when broadcasting the top 3 TV channels.

Dynamic point cloud delivery can provide the required interactivity and realism to six degrees of freedom (6DoF) interactive applications. However, dynamic point cloud rendering imposes stringent requirements (e.g., frames per second (FPS) and quality) that current hardware cannot handle. A possible solution is to convert point cloud into meshes before rendering on the head-mounted display (HMD). However, this conversion can induce degradation in quality perception such as a change in depth, level of detail, or presence of artifacts. This paper, as one of the first, presents an extensive subjective study of the effects of converting point cloud to meshes with different quality representations. In addition, we provide a novel in-session content rating methodology, providing a more accurate assessment as well as avoiding post-study bias. Our study shows that both compression level and observation distance have their influence on subjective perception. However, the degree of influence is heavily entangled with the content and geometry at hand. Furthermore, we also noticed that while end users are clearly aware of quality switches, the influence on their quality perception is limited. As a result, this has the potential to open up possibilities in bringing the adaptive video streaming paradigm to the 6DoF environment.

Point Cloud (PC) streaming has recently attracted research attention as it has the potential to provide six degrees of freedom (6DoF), which is essential for truly immersive media. PCs require high-bandwidth connections, and adaptive streaming is a promising solution to cope with fluctuating bandwidth conditions. Thus, understanding the impact of different factors in adaptive streaming on the Quality of Experience (QoE) becomes fundamental. Mixed Reality (MR) is a novel technology and has recently become popular. However, quality evaluations of PCs in MR environments are still limited to static images. In this paper, we perform a subjective study on four impact factors on the QoE of PC video sequences in MR conditions, including quality switches, viewing distance, and content characteristics. The experimental results show that these factors significantly impact QoE. The QoE decreases if the sequence switches to lower quality and/or is viewed at a shorter distance, and vice versa. Additionally, the end user might not distinguish the quality differences between two quality levels at a specific viewing distance. Regarding content characteristics, objects with lower contrast seem to provide better quality scores.

3D objects are important components in Mixed Reality (MR) environments as they allow users to inspect and interact with them in a six degrees of freedom (6DoF) system. Point clouds (PCs) and meshes are two common 3D object representations that can be compressed to reduce the delivered data at the cost of quality degradation. In addition, as the end users can move around in 6DoF applications, the viewing distance can vary. Quality assessment is necessary to evaluate the impact of the compressed representation and viewing distance on the Quality of Experience (QoE) of end users. This paper presents a demonstrator for subjective quality assessment of dynamic PC and mesh objects under different conditions in MR environments. Our platform allows conducting subjective tests to evaluate various QoE influence factors, including encoding parameters, quality switching, viewing distance, and content characteristics, with configurable settings for these factors.

Point cloud video streaming is a fundamental application of immersive multimedia. In it, objects represented as sets of points are streamed and displayed to remote users. Given the high bandwidth requirements of this content, small changes in the network and/or encoding can affect the users' perceived quality in unexpected manners. To tackle the degradation of the service as fast as possible, real-time Quality of Experience (QoE) assessment is needed. As subjective evaluations are not feasible in real time due to their inherent costs and duration, low-complexity objective quality assessment is a must. Traditional No-Reference (NR) objective metrics at client side are best suited to fulfill the task. However, they lack on accuracy to human perception. In this paper, we present a cluster-based objective NR QoE assessment model for point cloud video. By means of Machine Learning (ML)-based clustering and prediction techniques combined with NR pixel-based features (e.g., blur and noise), the model shows high correlations (up to a 0.977 Pearson Linear Correlation Coefficient (PLCC)) and low Root Mean Squared Error (RMSE) (down to 0.077 on a zero-to-one scale) towards objective benchmarks after evaluation on an adaptive streaming point cloud dataset consisting of sixteen source videos and 453 sequences in total.

Immersive media services, such as Augmented and Virtual Reality (AR/VR) are getting significant attention in recent years with the promise of bringing immersive experiences to end users. However, despite the remarkable advances in the field, AR/VR applications are mostly local and individual experiences. The main obstacle between current technology and future remote, multi-user AR/VR applications is the stringent end-to-end (E2E) latency requirement, which cannot exceed 20 ms to avoid motion sickness. Emerging AR/VR services put even more pressure on current network infrastructures, calling for considerable advancements toward fully cloud-native architectures. Cloud-based VR services, where participants can virtually interact across vast distances, remain a distant dream. Several challenges still arise concerning the deployment and management of VR services. This paper presents a Mixed-Integer Linear Programming (MILP) formulation for the efficient orchestration of VR services in fog-cloud infrastructures. The model considers Fog Computing (FC), an extension of cloud computing, and Segment Routing (SR), which leverages the source routing paradigm. The evaluation of realistic VR container-based service chains shows that deploying VR components hosted in a fog-cloud infrastructure can satisfy the 20 ms latency boundary.

Volumetric media has the potential to provide the six degrees of freedom (6DoF) required by truly immersive media. However, achieving 6DoF requires ultra-high bandwidth transmissions, which real-world wide area networks cannot provide today. Therefore, recent efforts have started to target efficient delivery of volumetric media, using a combination of compression and adaptive streaming techniques. It remains, however, unclear how the effects of such techniques on the user perceived quality can be accurately evaluated. In this paper, we present the results of an extensive objective and subjective quality of experience (QoE) evaluation of volumetric 6DoF streaming. We use PCC-DASH, a standards-compliant means for HTTP adaptive streaming of scenes comprising multiple dynamic point cloud objects. By means of a thorough analysis, we investigate the perceived quality impact of the available bandwidth, rate adaptation algorithm, viewport prediction strategy and user's motion within the scene. We determine which of these aspects has more impact on the user's QoE, and to what extent subjective and objective assessments are aligned.

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

The increasing popularity of head-mounted devices and 360° video cameras allows content providers to provide virtual reality (VR) video streaming over the Internet, using a two-dimensional representation of the immersive content combined with traditional HTTP adaptive streaming (HAS) techniques. However, since only a limited part of the video (i.e., the viewport) is watched by the user, the available bandwidth is not optimally used. Recent studies have shown the benefits of adaptive tile-based video streaming; rather than sending the whole 360° video at once, the video is cut into temporal segments and spatial tiles, each of which can be requested at a different quality level. This allows prioritization of viewable video content and thus results in an increased bandwidth utilization. Given the early stages of research, there are still a number of open challenges to unlock the full potential of adaptive tile-based VR streaming. The aim of this work is to provide an answer to several of these open research questions. Among others, we propose two tile-based rate adaptation heuristics for equirectangular VR video, which use the great-circle distance between the viewport center and the center of each of the tiles to decide upon the most appropriate quality representation. We also introduce a feedback loop in the quality decision process, which allows the client to revise prior decisions based on more recent information on the viewport location. Furthermore, we investigate the benefits of parallel TCP connections and the use of HTTP/2 as an application layer optimization. Through an extensive evaluation, we show that the proposed optimizations result in a significant improvement in terms of video quality (more than twice the time spent on the highest quality layer), compared to non-tiled HAS solutions.

The increasing popularity of head-mounted devices and 360° video cameras allows content providers to offer virtual reality video streaming over the Internet, using a relevant representation of the immersive content combined with traditional streaming techniques. While this approach allows the user to freely move her head, her location is fixed by the camera's position within the scene. Recently, an increased interest has been shown for free movement within immersive scenes, referred to as six degrees of freedom. One way to realize this is by capturing objects through a number of cameras positioned in different angles, and creating a point cloud which consists of the location and RGB color of a significant number of points in the three-dimensional space. Although the concept of point clouds has been around for over two decades, it recently received increased attention by ISO/IEC MPEG, issuing a call for proposals for point cloud compression. As a result, dynamic point cloud objects can now be compressed to bit rates in the order of 3 to 55 Mb/s, allowing feasible delivery over today's mobile networks. In this paper, we propose PCC-DASH, a standards-compliant means for HTTP adaptive streaming of scenes comprising multiple, dynamic point cloud objects. We present a number of rate adaptation heuristics which use information on the user's position and focus, the available bandwidth, and the client's buffer status to decide upon the most appropriate quality representation of each object. Through an extensive evaluation, we discuss the advantages and drawbacks of each solution. We argue that the optimal solution depends on the considered scene and camera path, which opens interesting possibilities for future work.

Adapting and tiling the streaming of virtual reality (VR) video content has the potential to reduce the ultra-high bandwidth requirements of this type of multimedia services. Towards that goal, the optimization of a number of aspects is currently actively being researched. Novel rate adaptation heuristics, sophisticated viewport prediction algorithms and streaming protocol optimizations have proven their value to improve certain aspect of the VR streaming chain. However, the interplay between all these different optimizations as well as their tradeoff has not yet been explored in an experimental playground. The purpose of this demonstrator is to provide a full end-to-end adaptive tile-based VR video streaming system where each of the optimization aspects can be tuned with and their effect illustrated on-site.

The demand of Virtual Reality (VR) video streaming to mobile devices is booming, as VR becomes accessible to the general public. However, the variability of conditions of mobile networks affects the perception of this type of high-bandwidth-demanding services in unexpected ways. In this situation, there is a need for novel performance assessment models fit to the new VR applications. In this paper, we present PERCEIVE, a two-stage method for predicting the perceived quality of adaptive VR videos when streamed through mobile networks. By means of machine learning techniques, our approach is able to first predict adaptive VR video playout performance, using network Quality of Service (QoS) indicators as predictors. In a second stage, it employs the predicted VR video playout performance metrics to model and estimate end-user perceived quality. The evaluation of PERCEIVE has been performed considering a real-world environment, in which VR videos are streamed while subjected to LTE/4G network condition. The accuracy of PERCEIVE has been assessed by means of the residual error between predicted and measured values. Our approach predicts the different performance metrics of the VR playout with an average prediction error lower than 3.7% and estimates the perceived quality with a prediction error lower than 4% for over 90% of all the tested cases. Moreover, it allows us to pinpoint the QoS conditions that affect adaptive VR streaming services the most.

Nowadays, news-based websites and portals provide significant amounts of multimedia content to accompany news stories and articles. Within this context, HTTP Adaptive Streaming is generally used to deliver video over the best-effort Internet, allowing smooth video playback and a good Quality of Experience (QoE). To stimulate user engagement with the provided content, such as browsing and switching between videos, reducing the video's startup time has become more and more important: while the current median load time is in the order of seconds, research has shown that user waiting times must remain below two seconds to achieve an acceptable QoE. We developed a framework for low-latent delivery of news-related video content, integrating four optimizations either at server-side, client-side, or at the application layer. Using these optimizations, the video's startup time can be reduced significantly, allowing user interaction and fast switching between available content. In this paper, we describe a proof of concept of this framework, using a large dataset of a major Belgian news provider. A dashboard is provided, which allows the user to interact with available video content and assess the gains of the proposed optimizations. Particularly, we demonstrate how the proposed optimizations consistently reduce the video's startup time in different mobile network scenarios. These reductions allow the news provider to improve the user's QoE, reducing the startup time to values well below two seconds in different mobile network scenarios.

Remote collaboration is common nowadays in conferencing, tele-health and remote teaching applications. To support these interactive use cases, Real-Time Communication (RTC) solutions, as the open-source WebRTC framework, are generally used. WebRTC is peer-to-peer by design, which entails that each sending peer needs to encode a separate, independent stream for each receiving peer in the remote session. This approach is therefore expensive in terms of number of encoders and not able to scale well for a large number of users. To overcome this issue, a WebRTC-compliant framework is proposed in this paper, where only a limited number of encoders are used at sender-side. Consequently, each encoder can transmit to a multitude of receivers at the same time. The conference controller, a centralized Selective Forwarding Unit (SFU), dynamically forwards the most suitable stream to each of the receivers, based on their bandwidth conditions. Moreover, the controller dynamically recomputes the encoding bitrates of the sender, to follow the long-term bandwidth variations of the receivers and increase the delivered video quality. The benefits of this framework are showcased using a demo implemented using the Jitsi-Videobridge software, a WebRTC SFU, for the controller and the Chrome browser for the peers. Particularly, we demonstrate how our framework can improve the received video quality up to 15% compared to an approach where the encoding bitrates are static and do not change over time.

Web portals, such as the one hosted by news providers, have recently started to provide significant amounts of multimedia content. To deliver this content over the best-effort Internet, HTTP Adaptive Streaming (HAS) is generally used, allowing smoother playback and a better Quality of Experience (QoE). To stimulate user engagement with the provided content, reducing the video's startup time has become more and more important: while the current median video load time is in the order of seconds, research has shown that user waiting times must remain below two seconds to achieve an acceptable QoE. In this work, we present a framework for low-latency delivery of news-related video content, integrating four optimizations either at server-side, client-side, or at the application layer. Most importantly, we propose to identify relevant content through user profiling, using proactive delivery and client-side caching to reduce the video startup time. By means of a large data set from a Belgian news provider, we show that the proposed framework can reduce the startup time from 4.6 s to 1.5 s (-74.6%) in a 3G scenario, at the cost of limited network overhead and additional complexity at server- and client-side.

Remote teaching applications are common nowa-days. Very often, these applications resemble video-on-demand streaming platforms rather than real virtual classrooms, where a group of students (the receivers) can remotely attend a live lecture held by a lecturer (the sender). To better support this live scenario, Real-Time Communication (RTC) solutions can be used. WebRTC is an open-source project for real-time browser- based conferencing, developed with a peer-to-peer architecture in mind. To use WebRTC, each receiver requires a dedicated encoder at sender-side. Using such approach is expensive in terms of encoders, and does not scale well for a large number of users. To overcome this issue, a WebRTC-compliant framework is proposed, where only a limited number of encoders are used. A centralized node, the conference controller, dynamically forwards the most suitable stream to the receivers, based on their bandwidth conditions. Moreover, the controller dynamically recomputes the encoding bitrates of the sender. This approach allows to closely follow the long-term bandwidth variations of the receivers, even with a limited number of encoders at sender-side. To evaluate the performance of the proposed framework in a realistic environment, a testbed has been implemented using the Chrome browser and the open-source Jitsi-Videobridge. In a scenario with 10 receivers and 3 encoders, and under realistic network conditions, the proposed framework improves the received video bitrate up to 11%, compared to a static solution where the encoding bitrates do not change over time.

Video streaming applications currently dominate Internet traffic. Particularly, HTTP Adaptive Streaming (HAS) has emerged as the dominant standard for streaming videos over the best-effort Internet, thanks to its capability of matching the video quality to the available network resources. In HAS, the video client is equipped with a heuristic that dynamically decides the most suitable quality to stream the content, based on information such as the perceived network bandwidth or the video player buffer status. The goal of this heuristic is to optimize the quality as perceived by the user, the so-called Quality of Experience (QoE). Despite the many advantages brought by the adaptive streaming principle, optimizing users’ QoE is far from trivial. Current heuristics are still suboptimal when sudden bandwidth drops occur, especially in wireless environments, thus leading to freezes in the video playout, the main factor influencing users’ QoE. This issue is aggravated in case of live events, where the player buffer has to be kept as small as possible in order to reduce the playout delay between the user and the live signal. In light of the above, in recent years, several works have been proposed with the aim of extending the classical purely client-based structure of adaptive video streaming, in order to fully optimize users’ QoE. In this article, a survey is presented of research works on this topic together with a classification based on where the optimization takes place. This classification goes beyond client-based heuristics to investigate the usage of server- and network-assisted architectures and of new application and transport layer protocols. In addition, we outline the major challenges currently arising in the field of multimedia delivery, which are going to be of extreme relevance in future years.

With the increasing popularity of multimedia-rich web portals, reducing latency has become more and more important. The current median web page load time is in the order of seconds, while research has shown that user waiting times must remain below two seconds to achieve optimal acceptance. In this paper, we analyzed a large dataset obtained from a major Belgian news provider, focusing on content popularity, user activity and user preference towards article news categories. Based on this analysis, we introduce the concept of personal delivery networks (PDNs), in which content is stored closer to the end user, at delivery caches in the edge of the core network or even in the access network. PDN nodes proactively prefetch and evict content on a per-user basis, opening opportunities for personalized low-latency delivery of multimedia-rich web applications. Initial results show that a PDN-based approach allows to significantly reduce the average latency.

The increased popularity of social media and mobile devices has radically changed the way people consume multimedia content online. As an example, users can experience the same event (e.g. a sports event or a concert) together using social media, even if they are not in the same physical location. Moreover, the introduction of the HTTP Adaptive Streaming principle has made it possible to deliver video over the best-effort Internet with consistent quality, even for mobile devices. One of the challenges within this context is the synchronization of multimedia playback among geographically distributed clients. To solve this issue, we propose a Web-based framework which allows to synchronize the playback of different clients. We also present a novel hybrid approach for adaptive streaming to allow fast synchronization among different clients, which relies on HTTP/2's server push feature in combination with sub-second video segments. In this paper, we detail the proposed framework and provide a comprehensive analysis of its performance. Experiments show that the novel hybrid approach can reduce synchronization time with 19.4% compared to standard adaptive streaming over HTTP/1.1 when bandwidth is limited to 2.5 Mb/s and an RTT of 150 ms. The gain increases even more when a higher throughput is available. The obtained results entail that the proposed framework can provide quality of experience for all users watching online video together.

HTTP Adaptive Streaming (HAS) is the de facto standard for video streaming services over the Internet. In HAS, each video is temporally segmented and stored in different qualities. The client selects the quality level for every video segment based on network conditions, allowing a smooth playback with the best possible Quality of Experience (QoE). Although results are promising, current solutions suffer from two problems. First, a low quality and large end-to-end latency are often observed in live streaming scenarios. Second, freezes in the video playout may occur in case of sudden drops of the available bandwidth. We reduced these issues using two complementary approaches. First, we reduced the live latency using the new HTTP/2 server push in combination with super-short segments. Second, we designed an OpenFlow-based network controller that prioritizes the delivery of particular segments to avoid freezes at the clients. The proof-of-concept shows the results obtained when two clients stream a video under varying network conditions. By monitoring the clients' behavior, it is possible to understand the gains brought by the proposed approaches. Particularly, we demonstrate how our solutions consistently reduce the live latency in high round-trip time networks and video freezes caused by network congestion. These results represent a major improvement for the QoE of the final users.

HTTP Adaptive Streaming (HAS) is the de facto standard for over-the-top video streaming. In HAS, video content is encoded at multiple quality levels and temporally divided into multiple segments. The client can select the quality level for every video segment, allowing smoother playback and a better Quality of Experience (QoE). Although results are promising, current solutions often suffer from high round-trip time (RTT) cycles in mobile networks. This is especially true for scalable video coding (SVC), where multiple requests are required to retrieve a single video segment. Meanwhile, the IETF has standardized the HTTP/2 protocol since February 2015, providing new features that allow a reduction of the page load time in Web browsing. In this paper, we propose a novel approach based on HTTP/2's server push feature to actively push the base layer of live, SVC-encoded content from server to client. This allows to eliminate one RTT cycle for every video segment, which has a significant impact on the user's QoE. Evaluating the proposed approach, we show that compared with HTTP/1.1, an improvement of 65.42% can be achieved for the average video quality in high-RTT networks. Compared to an AVC-based solution, the freeze frequency and duration are reduced by 54.55% and 53.06% respectively, while the loss in video quality is limited to 4.51%. Since playout freezes should be avoided at the cost of a lower video quality, we conclude that the proposed approach beneficially impacts the user's QoE.

HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for Over-The-Top video streaming. A HAS video consists of multiple segments, encoded at multiple quality levels. Allowing the client to select the quality level for every segment, a smoother playback and a higher Quality of Experience (QoE) can be perceived. Although results are promising, current quality selection heuristics are generally hard coded. Fixed parameter values are used to provide an acceptable QoE under all circumstances, resulting in suboptimal solutions. Furthermore, many commercial HAS implementations focus on a video-on-demand scenario, where a large buffer size is used to avoid play-out freezes. When the focus is on a live TV scenario however, a low buffer size is typically preferred, as the video play-out delay should be as low as possible. Hard coded implementations using a fixed buffer size are not capable of dealing with both scenarios. In this paper, the concept of reinforcement learning is introduced at client side, allowing to adaptively change the parameter configuration for existing rate adaptation heuristics. Bandwidth characteristics are taken into account in the decision process, thus allowing to improve the client's bandwidth-awareness. Focus in this paper is on actively reducing the average buffer filling, evaluating results for two heuristics: the Microsoft IIS Smooth Streaming heuristic and the QoE-driven Rate Adaptation Heuristic for Adaptive video Streaming by Petrangeli et al. We show that using the proposed learning-based approach, the average buffer filling can be reduced by 8.3% compared to state of the art, while achieving a comparable level of QoE.

HTTP Adaptive Streaming (HAS) is today the number one video technology for over-the-top video distribution. In HAS, video content is temporally divided into multiple segments and encoded at different quality levels. A client selects and retrieves per segment the most suited quality version to create a seamless playout. Despite the ability of HAS to deal with changing network conditions, HAS-based live streaming often suffers from freezes in the playout due to buffer under-run, low average quality, large camera-to-display delay, and large initial/channel-change delay. Recently, IETF has standardized HTTP/2, a new version of the HTTP protocol that provides new features for reducing the page load time in Web browsing. In this paper, we present ten novel HTTP/2-based methods to improve the quality of experience of HAS. Our main contribution is the design and evaluation of a push-based approach for live streaming in which super-short segments are pushed from server to client as soon as they become available. We show that with an RTT of 300 ms, this approach can reduce the average server-to-display delay by 90.1% and the average start-up delay by 40.1%.