Receiver-side video super-resolution (VSR) offers a promising approach to improving visual quality in real-time communication (RTC) systems without increasing transmission bandwidth. However, deploying learning-based VSR models inside browser-native WebRTC pipelines introduces tight latency constraints and complex trade-offs between perceptual quality, computation, and network conditions that are difficult to study offline. This paper presents a live demo of VSR-Bench, an interactive, browser-native WebRTC platform for end-to-end exploration of real-time receiver-side VSR. The demo integrates state-of-the-art VSR models directly into the WebRTC receiver pipeline using TensorFlow.js and supports multiple execution architectures (single-threaded and multi-threaded) as well as GPU backends (WebGL and WebGPU). Through a web-based interface, users can configure codecs, models, scale factors, and runtime settings, and observe their impact on visual quality and end-to-end latency in real time. During execution, the demo exposes end-to-end latency and live network statistics (bandwidth, jitter, packet loss); after execution, it presents per-stage runtime breakdowns, perceptual quality metrics (VMAF), and time-series plots of the network statistics observed during the demo. By enabling hands-on comparison between classical interpolation and learning-based VSR under realistic network conditions, the demo provides intuitive insight into when and how receiver-side VSR improves Quality of Experience in browser-based RTC systems.

Research on adaptive streaming of volumetric video is constrained by the absence of a practical evaluation platform for MPEG-I V3C content encoded using V-PCC. In this framework, dynamic point clouds are represented as multiple synchronized video components, including geometry, occupancy, and attributes, each exhibiting distinct adaptation trade-offs. At the same time, immersive six-degrees-of-freedom navigation further necessitates viewpoint-dependent delivery and fine-grained adaptation, motivating spatial tiling and independent control over subsets of the encoded content. Despite these requirements, no publicly available system supports adaptive streaming of spatially tiled V3C content with independent control over V-PCC components, and existing approaches are typically tied to real-time decoding and rendering pipelines, making experimentation slow and difficult to reproduce. This paper presents Prism, an open-source, trace-driven simulator for adaptive streaming of V3C bitstreams generated by V-PCC encoders using MPEG-DASH-aligned manifests that incorporate V3C-aware and tile-level signaling for research and evaluation purposes. Prism models DASH adaptive control logic while abstracting away decoding and rendering, allowing hours of V3C streaming to be evaluated in minutes of simulation time, and supports independent quality selection for each spatial tile and each V-PCC video component under realistic network and viewing traces. By enabling fast and reproducible experimentation on encoded V3C content, Prism fills a critical gap for research on adaptive streaming of volumetric video.

Dynamic point clouds enable immersive six-degrees-of-freedom experiences but require complex content preparation pipelines to support efficient adaptive delivery at scale. In practice, workflows for dynamic point cloud streaming remain fragmented: spatial tiling, encoding, segmentation, and adaptive streaming signaling are often handled by disconnected tools, ad hoc scripts, or platform-dependent binaries that are difficult to obtain, compile, and reproduce, particularly in Linux-based research environments. This paper presents V3CTK, an end-to-end content preparation toolkit for tiled dynamic point cloud streaming that automates these steps with minimal manual intervention. V3CTK unifies segment-adaptive spatial tiling, per-tile sequence-level V-PCC encoding, GoF-aligned V3C segmentation, and MPEG-DASH MPD generation within a single modular pipeline, while still allowing individual stages to be executed independently when needed. The generated bitstream segments and manifests follow MPEG-I V3C/V-PCC (ISO/IEC 23090-5) and MPEG-DASH (ISO/IEC 23009-1) signaling conventions, while incorporating explicit extensions to support tiled and component-level streaming. In addition to unified V3C bitstreams, the pipeline supports component-wise bitstream generation with multiple representations per component, and provides a lightweight multiplexing step to reconstruct a fully decodable unified V3C bitstream from per-component streams prior to playback. V3CTK exposes finegrained parameter control through command-line and web-based interfaces, offering a unified workflow for the research community and simplifying setup through Dockerized encoding.

Volumetric media streaming is a key enabler of immersive 6DoF applications such as virtual reality and interactive telepresence. Dynamic point clouds, standardized by MPEG through Video-based Point Cloud Compression (V-PCC), offer an efficient representation for such content. However, existing datasets and quality evaluation studies are largely human-centric and fail to capture the heterogeneity of real-world volumetric scenes in terms of object category, motion dynamics, and geometric complexity, limiting content-aware systems analysis. In this paper, we introduce QUEST-PCC1, an open-source reference dataset for objective quality and rate-distortion analysis of heterogeneous dynamic point clouds encoded using the MPEG V-PCC standard. QUEST-PCC spans six object categories, including articulated, rigid, and non-human objects, and covers a wide range of motion and structural complexity. The dataset provides reconstructed point clouds, compressed V-PCC bitstreams, and standardized per-frame MPEG PCC quality metrics across multiple rate levels, yielding content-dependent bitrate ladders that span from sub-1 Mbps to approximately 80 Mbps. Through systematic sequence-level and category-level analysis, we show that encoding bitrate and reconstruction quality are governed primarily by dynamic surface topology and structural complexity rather than point count alone, revealing content-dependent rate-distortion behavior that is not captured by existing human-centric datasets and is critical for content-aware compression and adaptive volumetric streaming.

Multi-CDN strategies are crucial for ensuring high Quality of Experience (QoE) in adaptive video streaming. Yet existing approaches-whether heuristic or learning-based-largely depend on aggregated client metrics, leading to coarse-grained CDN selection that constrains QoE and neglects operational costs. To address these shortcomings, we propose CADENCE, a multi-agent reinforcement learning framework built on Centralized Training with Decentralized Execution (CTDE). CADENCE deploys per-client agents to perform fine-grained, real-time CDN selection tailored to each streaming session. Unlike prior work, CADENCE jointly optimizes client-side QoE and content provider-side costs while proactively mitigating CDN overload. Through extensive high-idelity trace-driven emulation, we demonstrate that CADENCE signiicantly outperforms state-of-the-art baselines, improving average VMAF by up to 21% with minimal rebuffering and reducing operational costs by 35%.

Volumetric media in the form of dynamic point clouds is increasingly adopted for immersive applications such as virtual reality, telepresence, digital twins, and interactive 3D experiences, yet practical support for browser-native visualization remains limited. Existing solutions often rely on heavyweight native players, offline preprocessing, or specialized plugins, creating a significant barrier to accessibility and deployment at scale. In this demo, we present NAVIS, a fully browser-based volumetric media player that enables interactive playback and exploration of dynamic PLY point-cloud sequences using standard web technologies. NAVIS combines WebGL-based rendering with a parallel web-worker parsing pipeline, buffered playback with adaptive concurrency control, and interactive navigation modes, allowing large volumetric datasets to be visualized smoothly directly within the browser. We instrument NAVIS to expose key performance metrics, including startup delay, parsing latency, and stall duration, and evaluate it across multiple volumetric datasets at 30 fps and 60 fps. Our results show that increasing worker parallelism substantially reduces startup delay and mitigates playback stalls, demonstrating that high-performance volumetric media visualization is feasible on the web without native dependencies. NAVIS provides a practical and extensible foundation for future browser-native immersive media systems.

Forward Error Correction (FEC) remains essential for protecting video streaming against packet loss, yet most real deployments still rely on static, coarse-grained configurations that cannot react to rapid shifts in loss rate, goodput, or client buffer levels. These rigid settings often create inefficiencies: unnecessary redundancy that suppresses throughput during stable periods, and insufficient protection during bursty losses, especially when shallow buffers and oversized blocks increase stall risk. To address these challenges, we present TAROT, a cross-layer, optimization-driven FEC controller that selects redundancy, block size, and symbolization on a per-segment basis. TAROT is codec-agnostic--supporting Reed-Solomon, RaptorQ, and XOR-based codes--and evaluates a pre-computed candidate set using a fine-grained scoring model. The scoring function jointly incorporates transport-layer loss and goodput, application layer buffer dynamics, and block-level timing constraints to penalize insufficient coverage, excessive overhead, and slow block completion. To enable realistic testing, we extend the SABRE simulator 1 with two new modules: a high-fidelity packet-loss generator that replays diverse multi-trace loss patterns, and a modular FEC benchmarking layer supporting arbitrary code/parameter combinations. Across Low-Latency Live (LLL) and Video-on-Demand (VoD) streaming modes, diverse network traces, and multiple ABR algorithms, TAROT reduces FEC overhead by up to 43% while improving perceptual quality by 10 VMAF units with minimal rebuffering, achieving a stronger overhead-quality balance than static FECs.

The ETSI TS 103 998 content steering standard enables runtime switching across multiple Content Delivery Networks (CDNs), but current deployments still rely on static heuristics that struggle under dynamic and low-latency network conditions. In this demo, we present the integration of CADENCE, a fine-grained multi-agent multi-CDN controller, directly into the Dash.js reference player, transforming it into a practical experimentation platform for CDN steering. The enhanced player exposes real-time CDN telemetry, per-CDN QoE scoring, and runtime strategy selection, enabling transparent and high-fidelity comparison of steering approaches under identical playback conditions. We additionally include an optional short-horizon forecasting module as a proof-of-concept extension and use controlled ablation experiments to show how forecasted telemetry can mitigate the impact of delayed feedback with negligible overhead. Overall, the demo illustrates how CADENCE —and more broadly, learning-based or multi-agent steering controllers—can be seamlessly embedded into Dash.js to study robust multi-CDN behavior in realistic deployments.

Real-time interaction with multimodal Large Language Models (M-LLMs) remains impractical because conventional serving pipelines impose a receive-then-process barrier: video must be fully uploaded and preprocessed before inference can begin, leading to accelerator cold-start and high end-to-end latency. We present RabbitLLM, an end-to-end serving architecture that enables network-compute pipelining by coupling progressive video transport with transport-triggered incremental KV-cache construction. RabbitLLM streams video frames using WebRTC and immediately prefixes arriving visual chunks into the model state, overlapping transmission with prefilling while deferring decoding until a warm cache is available. Across realistic 4G/5G traces, RabbitLLM reduces interactive end-to-end delay by 43.5% compared to an upload-first TCP baseline while preserving output quality (BERTScore F1 > 0.82). RabbitLLM also reduces per-frame transmission delay by 95.4% and bandwidth consumption by 99.1%. These results suggest that low-latency multimodal serving requires co-design between streaming transport and inference execution, rather than optimizing either layer in isolation.

The QUIC transport protocol represents a significant evolution in web transport technologies, offering improved performance and reduced latency compared to traditional protocols like TCP. Given the growing number of QUIC implementations, understanding their performance, particularly in video streaming contexts, is essential. This paper presents a comprehensive analysis of various QUIC implementations, focusing on their transport-layer congestion control (CC) performance and its impact on HTTP Adaptive Streaming (HAS) in single-server, multi-client environments. Through extensive trace-driven experiments, we explore how different QUIC CCs impact adaptive bitrate (ABR) algorithms in two video streaming scenarios: video-on-demand (VoD) and low-latency live streaming (LLL). Our study aims to shed light on the impact of QUIC CC implementations, queuing strategies, and cooperative versus competitive dynamics of QUIC streams on user QoE under diverse network conditions. Our results demonstrate that identical CC algorithms across different QUIC implementations can lead to significant performance variations, directly impacting the QoE of video streaming sessions. These findings offer valuable insights into the effectiveness of various QUIC implementations and their implications for optimizing QoE, underscoring the need for intelligent cross-layer designs that integrate QUIC CC and ABR schemes to enhance overall streaming performance.

The rapid adoption of QUIC as a transport protocol has transformed content delivery by reducing latency, enhancing congestion control (CC), and enabling more efficient multiplexing. With the advent of 5G networks, which support ultra-low latency and high bandwidth, streaming high-resolution video at 4K and beyond has become increasingly viable. However, optimizing Quality of Experience (QoE) in mobile networks remains challenging due to the complex interactions among Adaptive Bit Rate (ABR) schemes at the application layer, CC algorithms at the transport layer, and Radio Link Control (RLC) queuing at the link layer in the 5G network. While prior studies have largely examined these components in isolation, this work presents a comprehensive analysis of the impact of modern active queue management (AQM) strategies, such as RED and L4S, on video streaming over diverse QUIC implementations—focusing particularly on their interaction with the RLC buffer in 5G environments and the interplay between CC algorithms and ABR schemes. Our findings demonstrate that the effectiveness of AQM strategies in improving video streaming QoE is intrinsically linked to their dynamic interaction with QUIC implementations, CC algorithms and ABR schemes—highlighting that isolated optimizations are insufficient. This intricate interdependence necessitates holistic, cross-layer adaptive mechanisms capable of real-time coordination between network, transport and application layers, which are crucial for leveraging the capabilities of 5G networks to deliver robust, adaptive, and high-quality video.

The increasing demand for high-quality real-time 360° video streams from mobile platforms, such as 5G-connected Unmanned Aerial Vehicles (UAVs), is challenging modern B5G networks. Vehicular mobility and fluctuating conditions in high-altitude, high-speed scenarios, known as high volatility, complicate maintaining an effective Quality of Experience (QoE) for cellular networks. This work introduces FlyBit, a Deep Reinforcement Learning (DRL)-based bitrate selection framework for live 360° video streaming in 5G-connected UAV applications, designed to enhance video quality, reduce packet loss, and minimize End-to-End (E2E) latency. We developed and deployed a real-world testbed to evaluate the impact of dynamic network conditions, UAV mobility, and trajectory on streaming performance, analyzing FlyBit with real-world data. Experimental results show that FlyBit improves Video Multimethod Assessment Fusion (VMAF) by ~29% and average bitrate by ~50%, while maintaining low latency and packet loss compared to baseline approaches, demonstrating its ability to adjust bitrate in real-time and significantly improve QoE for ultra-low-latency video streaming.

Multi-CDN strategies have become increasingly important in enhancing Quality of Experience (QoE) for adaptive video streaming. The recent development of the content steering standard (ETSI TS 103 998) aims to facilitate real-time decision-making about the best-performing CDN by gathering statistics from both players and CDNs. However, this task presents significant challenges. Existing solutions for CDN selection rely on heuristics, which often fail to adapt to diverse network conditions and suffer from issues such as prolonged CDN switching delays and/or complex implementation requirements, resulting in poor QoE. To address these limitations, we present StreamWise---a learning-based solution for real-time CDN selection that works effectively for both on-demand and live adaptive video streaming. StreamWise implements on the content steering standard, leveraging a deep reinforcement learning (DRL) framework to learn and predict in real-time the optimal CDN selection policy by continuously interacting with the environment. Our solution adapts in real-time to network conditions, content characteristics, and viewer requirements ensuring the delivery of high-quality content to users. We evaluate StreamWise through extensive trace-driven emulation-based experiments, demonstrating its superior performance compared to conventional CDN selection strategies. Our results demonstrate a significant improvement in VMAF by ~8.5% with ~1.5x higher average bitrate and QoE improvement of ~48% with minimal rebuffering events for video-on-demand streaming and an improvement in VMAF by ~7%, with ~1.2x higher average bitrate and a QoE improvement of ~22% while substantially reducing the rebuffering events by ~10× for live streaming.

This paper tackles the challenge of designing and implementing a complete, operational, and extensible web-based virtual reality (VR) system for stored six degrees-of-freedom (6DoF) content. We present a unique, open-source volumetric video streaming system, termed WIDE-VR, that leverages cutting-edge web technologies, including a WebGL-based rendering engine, Draco's real-time inbrowser decoder, and HTTP/3 over QUIC transport. We also explore various adaptive streaming and viewport prediction strategies to achieve improvements in bandwidth efficiency, reduced rebuffering, and minimized quality fluctuations. Our experimental results demonstrate the system's capabilities and potential in delivering immersive, high-quality VR experiences directly via web platforms, fostering broader accessibility and scalability for VR applications.

Field of view (FoV) prediction is crucial for optimizing 6-DoF dynamic point cloud-based volumetric video (PCV) streaming. By accurately predicting which tiles fall within the viewer's region of interest, FoV prediction enables adaptive bitrate (ABR) algorithms to allocate higher bitrates to likely viewed tiles while assigning lower bitrates to less critical areas. This improves bandwidth efficiency and enhances the quality of experience (QoE) by aligning bitrate allocation with the viewer's focus. However, current 6-DoF salience-aware FoV prediction models face challenges related to high latency, computational costs, and a lack of complex datasets with detailed FoV traces, hindering the development of more effective real-time predictors. To address these challenges, we propose the LL-Sparse family, a suite of three solutions for direct tile salience score prediction: LL-Adapter, an extension of HMD-trajectory-based (HTB) models, such as GRUs, tailored for tile scoring; LL-PointNet, which integrates a GRU with PointNet to enhance salience-aware prediction; and LL-SparseConv, a scalable variant of LL-PointNet that employs sparse convolution in place of PointNet, serving as a proof of concept. These models strike a balance between practical performance and theoretical advancements in tile salience prediction. Furthermore, we introduce the MazeLab dataset, a novel, large-scale dynamic point cloud dataset that mimics real-world PCV scenarios to effectively benchmark FoV prediction models. Experimental results highlight the LL-Sparse family's exceptional scalability, reduced latency, and enhanced accuracy, establishing it as a promising solution for efficient real-time volumetric media applications.

Point cloud video datasets enriched with user behavior traces are critical for advancing tile-based HTTP adaptive streaming (HAS) systems, particularly those reliant on large training data for player modules like Field of View (FoV) prediction. This paper presents MazeLab, a dynamic volumetric video dataset comprising a feature-rich point cloud representation of a large maze environment. The dataset captures navigation traces from 15 participants interacting with 15 distinct maze variants, categorized into seven classes designed to elicit specific behavioral characteristics such as navigation patterns, attention hotspots, and interaction dynamics. By incorporating diverse environmental designs, each targeting unique user responses, MazeLab provides a comprehensive behavioral repository, enabling robust testing and optimization of HAS-driven volumetric video systems in complex scenarios. The dataset is publicly available here.

The recent introduction of the ETSI TS 103 998 content steering standard marks a significant milestone in the evolution of media delivery for content providers. This standard simplifies the process of utilizing multiple Content Delivery Networks (CDNs), a key requirement for managing large-scale client bases. It offers clear guidelines for real-time CDN state switching, which enhances the user experience by dynamically selecting the most suitable CDN based on changing network conditions. In contrast, current industry solutions often resemble load balancing techniques, relying on heuristic approaches that are manually crafted and unable to adapt effectively to varying network environments. These static solutions frequently fail to optimize the user experience in real-time, especially when faced with diverse and unpredictable network conditions. In this paper, we utilize our previous work StreamWise---a DRL-based multi-CDN solution that outperforms existing state-of-the-art methods, offering superior adaptability and efficiency. We integrate this solution into the Dash.js reference player. Through a comprehensive demonstration, we illustrate how StreamWise, or any other multi-CDN solution, can be seamlessly incorporated into Dash.js, providing a benchmark for future developments in multi-CDN switching solutions. Experimental results in the wild demonstrate the performance of various multi-CDN solutions for Dash.js in Video on Demand (VoD) streaming mode. On average, StreamWise provides a ~78% improvement in VMAF compared to other solutions, while also ensuring smooth quality transitions., while also ensuring smooth quality transitions.

QUIC is rapidly emerging as the de facto standard for video streaming, particularly with the advent of 5G technology enabling the delivery of high resolution 4K content. However, sudden fluctuations in network capacity between 4G and 5G networks can occur due to various factors, such as network congestion, user mobility, or physical obstructions, leading to rapid changes in available bandwidth, which pose significant challenges for HTTP Adaptive Streaming (HAS) sessions that rely on more stable data rates for smooth playback, often resulting in interruptions and degraded Quality of Experience (QoE). Furthermore, current QUIC implementations are characterized by static configuration parameters, including constant pacing rates and static buffer sizes. This rigidity limits the server's ability to adapt dynamically to the client's real-time network conditions, making it challenging to prevent playback stalls and improve user QoE. To address these issues, we propose Connection-Aware QUIC (CAQ), which utilizes encrypted client playback statistics to dynamically adjust server parameters and implement an adaptive pacing rate. This approach aims to optimize streaming performance and enhance user experience, even in highly fluctuating network conditions. To evaluate the performance of CAQ, we conduct a series of trace-driven experiments across two key streaming modes: Video-on-Demand (VoD) and Low-Latency Live (LLL). Experimental results demonstrate that CAQ improves the VMAF by ~4%, QoEyin by ~41% while reducing rebuffering duration by ~8% for VoD mode and improves the VMAF by ~7%, QoEyin by ~49% with comparable rebuffering for LLL mode.

QUIC is rapidly emerging as the de-facto standard for HTTP Adaptive Streaming (HAS). QUIC relies on heuristic-based congestion control algorithms which were predominantly designed for TCP and thus have poor generalizability, ultimately degrading the user's Quality of Experience (QoE). Existing learning-based solutions for TCP are not pluggable and require a lot of engineering work, impacting their integration with QUIC. To tackle these challenges, we develop Aero--- the first learning-based plug-and-play congestion control algorithm for QUIC that considers the varying network statistics and can easily be integrated with any QUIC implementation. To analyze the performance of Aero, we conduct a series of comprehensive trace-driven experiments and evaluate its efficiency not only from a congestion control perspective but also the impact it has on the client-driven adaptive bitrate scheme (ABR). Experimental results demonstrate that Aero improves VMAF by ~12% with ~65% less rebuffering for low latency live streaming sessions compared to its competitors. Moreover, Aero excels in terms of delivery rate and delay across different network conditions.

Volumetric video streaming technologies are the future of immersive media services such as virtual, augmented, and mixed-reality experiences. The challenges surrounding such technologies are tremendous due to the high network bandwidth needed to produce high-quality and low-latency streams. Many techniques and solutions have been proposed across the streaming workflow to mitigate such challenges. To better understand and organize these developments, this survey adopts an architectural framework to showcase current and emerging techniques and solutions for volumetric video streaming while highlighting some of their characteristic challenges and opportunities.

In the realm of 360° video streaming, three paramount challenges arise: accurate prediction of the field of view, efficient bandwidth utilization, and seamless viewport sharing. Current solutions often neglect the latter aspect of 360° videos, thereby necessitating ultra-low latency, high quality, and real-time rendering. The considerable size of 360° videos further intensifies these challenges. To tackle these issues, we introduce LCR360, a deep learning-based neural network solution designed for accurate head movement prediction. Our solution is deployed on E3PO [9], an end-to-end open-source evaluation platform dedicated to 360° video-on-demand streaming. By integrating simulated streams into the Unreal Engine, we provide an immersive experience that enables users to share viewports seamlessly. Moreover, LCR360 surpasses its competitors by ~5% in the S-metric, a measure that combines the MSE of video quality, bandwidth, and storage costs.

Adaptive bitrate (ABR) schemes enable streaming clients to adapt to time-varying network/device conditions for a stall-free viewing experience. Most ABR schemes use manually tuned heuristics or learning-based methods. Heuristics are easy to implement but do not always perform well, whereas learning-based methods generally perform well but are difficult to deploy on low-resource devices. To make the most out of both worlds, we earlier developedAhaggar, a learning-based scheme executing on the server side that provides quality-aware bitrate guidance to streaming clients running their own heuristics.Ahaggar's novelty is the meta reinforcement learning approach taking network conditions, clients' statuses and device resolutions, and streamed content as input features to perform bitrate guidance.Ahaggaruses the new Common Media Client/Server Data (CMCD/SD) protocols to exchange the necessary metadata between the servers and clients. WhileAhaggarwas a significant step forward, in this study, we focus on three open areas, namely, ($i$) exploring the performance ofAhaggarin a heterogeneous environment including bothAhaggarand non-Ahaggarclients with varied network conditions and device resolutions, and ($ii$) quantifying the impact of device resolutions on QoE withAhaggar. We thoroughly investigate these areas and report our findings. We also ($iii$) discuss theAhaggardesign choices. Experiments on an open-source system show thatAhaggaradapts to unseen conditions fast and outperforms its competitors in several viewer experience metrics.

5G and 6G networks are expected to support various novel emerging adaptive video streaming services (e.g., live, VoD, immersive media, and online gaming) with versatile Quality of Experience (QoE) requirements such as high bitrate, low latency, and sufficient reliability. It is widely agreed that these requirements can be satisfied by adopting emerging networking paradigms like Software-Defined Networking (SDN), Network Function Virtualization (NFV), and edge computing. Previous studies have leveraged these paradigms to present network-assisted video streaming frameworks, but mostly in isolation without devising chains of Virtualized Network Functions (VNFs) that consider the QoE requirements of various types of Multime-dia Services (MS). To bridge the aforementioned gaps, we first introduce a set of multimedia VNFs at the edge of an SDN-enabled network, form diverse Service Function Chains (SFCs) based on the QoE requirements of different MS services. We then propose SARENA, an _S_FC-enabled ArchitectuRe for adaptive VidEo StreamiNg Applications. Next, we formulate the problem as a central scheduling optimization model executed at the SDN controller. We also present a lightweight heuristic solution consisting of two phases that run on the SDN controller and edge servers to alleviate the time complexity of the optimization model in large-scale scenarios. Finally, we design a large-scale cloud-based testbed including 250 HTTP Adaptive Streaming (HAS) players requesting two popular MS applications (i.e., live and VoD), conduct various experiments, and compare its effectiveness with baseline systems. Experimental results illustrate that SARENA outperforms baseline schemes in terms of users' QoE by at least 39.6%, latency by 29.3%, and network utilization by 30% in both MS services.

In today’s Internet, HTTP Adaptive Streaming (HAS) is the mainstream standard for video streaming, which switches the bitrate of the video content based on an Adaptive BitRate (ABR) algorithm. An effective Quality of Experience (QoE) assessment metric can provide crucial feedback to an ABR algorithm. However, predicting such real-time QoE on the client side is challenging. The QoE prediction requires high consistency with the Human Visual System (HVS), low latency, and blind assessment, which are difficult to realize together. To address this challenge, we analyzed various characteristics of HAS systems and propose a non-uniform sampling metric to reduce time complexity. Furthermore, we design an effective QoE metric that integrates resolution and rebuffering time as the Quality of Service (QoS), as well as spatiotemporal output from a deep neural network and specific switching events as content information. These reward and penalty features are regressed into quality scores with a Support Vector Regression (SVR) model. Experimental results show that the accuracy of our metric outperforms the mainstream blind QoE metrics by 0.3, and its computing time is only 60% of the video playback, indicating that the proposed metric is capable of providing real-time guidance to ABR algorithms and improving the overall performance of HAS. The QoE model is released on https://github.com/lcysyzxdxc/ASPECT.

Adaptive bitrate (ABR) schemes enable streaming clients to adapt to time-varying network/device conditions to achieve a stall-free viewing experience. Most ABR schemes use manually tuned heuristics or learning-based methods. Heuristics are easy to implement but do not always perform well, whereas learning-based methods generally perform well but are difficult to deploy on low-resource devices. To make the most out of both worlds, we develop Ahaggar, a learning-based scheme running on the server side that provides quality-aware bitrate guidance to streaming clients running their own heuristics. Ahaggar's novelty is the meta reinforcement learning approach taking network conditions, clients' statuses and device resolutions, and streamed content as input features to perform bitrate guidance. Ahaggar uses the new Common Media Client/Server Data (CMCD/SD) protocols to exchange the necessary metadata between the servers and clients. Experiments on an open-source system show that Ahaggar adapts to unseen conditions fast and outperforms its competitors in several viewer experience metrics.

Video content in Live HTTP Adaptive Streaming (HAS) is typically encoded using a pre-defined, fixed set of bitrate-resolution pairs (termed Bitrate Ladder), allowing play-back devices to adapt to changing network conditions using an adaptive bitrate (ABR) algorithm. However, using a fixed one-size-fits-all solution when faced with various content complexities, heterogeneous network conditions, viewer device resolutions and locations, does not result in an overall maximal viewer quality of experience (QoE). Here, we consider these factors and design LALISA, an efficient framework for dynamic bitrate ladder optimization in live HAS. LALISA dynamically changes a live video session’s bitrate ladder, allowing improvements in viewer QoE and savings in encoding, storage, and bandwidth costs. LALISA is independent of ABR algorithms and codecs, and is deployed along the path between viewers and the origin server. In particular, it leverages the latest developments in video analytics to collect statistics from video players, content delivery networks and video encoders, to perform bitrate ladder tuning. We evaluate the performance of LALISA against existing solutions in various video streaming scenarios using a trace-driven testbed. Evaluation results demonstrate significant improvements in encoding computation (24.4%) and bandwidth (18.2%) costs with an acceptable QoE.

Bandwidth prediction is critical in any Real-time Communication (RTC) service or application. This component decides how much media data can be sent in real time. Subsequently, the video and audio encoder dynamically adapts the bitrate to achieve the best quality without congesting the network and causing packets to be lost or delayed. To date, several RTC services have deployed the heuristic-based Google Congestion Control (GCC), which performs well under certain circumstances and falls short in some others. In this paper, we leverage the advancements in reinforcement learning and propose BoB (Bang-on-Bandwidth) — a hybrid bandwidth predictor for RTC. At the beginning of the RTC session, BoB uses a heuristic-based approach. It then switches to a learning-based approach. BoB predicts the available bandwidth accurately and improves bandwidth utilization under diverse network conditions compared to the two winning solutions of the ACM MMSys'21 grand challenge on bandwidth estimation in RTC. An open-source implementation of BoB is publicly available for further testing and research.

While most of the HTTP adaptive streaming (HAS) traffic continues to be video-on-demand (VoD), more users have started generating and delivering live streams with high quality through popular online streaming platforms. Typically, the video contents are generated by streamers and being watched by large audiences which are geographically distributed far away from the streamers locations. The locations of streamers and audiences create a significant challenge in delivering HAS-based live streams with low latency and high quality. Any problem in the delivery paths will result in a reduced viewer experience. In this paper, we propose HxL3, a novel architecture for low-latency live streaming. HxL3 is agnostic to the protocol and codecs that can work equally with existing HAS-based approaches. By holding the minimum number of live media segments through efficient caching and prefetching policies at the edge, improved transmissions, as well as transcoding capabilities, HxL3 is able to achieve high viewer experiences across the Internet by alleviating rebuffering and substantially reducing initial startup delay and live stream latency. HxL3 can be easily deployed and used. Its performance has been evaluated using real live stream sources and entities that are distributed worldwide. Experimental results show the superiority of the proposed architecture and give good insights into how low latency live streaming is working.

In this paper, we introduce a CMCD-Aware per-Device bitrate LADder construction (CADLAD) that leverages the Common Media Client Data (CMCD) standard to address the above issues. CADLAD comprises components at both client and server sides. The client calculates the top bitrate (tb) — a CMCD parameter to indicate the highest bitrate that can be rendered at the client — and sends it to the server together with its device type and screen resolution. The server decides on a suitable bitrate ladder, whose maximum bitrate and resolution are based on CMCD parameters, to the client device with the purpose of providing maximum QoE while minimizing delivered data. CADLAD has two versions to work in Video on Demand (VoD) and live streaming scenarios. Our CADLAD is client agnostic; hence, it can work with any players and ABR algorithms at the client. The experimental results show that CADLAD is able to increase the QoE by 2.6x while saving 71% of delivered data, compared to an existing bitrate ladder of an available video dataset. We implement our idea within CAdViSE — an open-source testbed for reproducibility.

Designing a cost-effective, scalable, and flexible architecture that supports low latency and high quality live video streaming is still a challenge for Over-The-Top (OTT) service providers. To cope with this issue, this paper leverages Peer-to-Peer (P2P), Content Delivery Network (CDN), edge computing, Network Function Virtualization (NFV), and distributed video transcoding paradigms to introduce a hybRId P2P-DN arcHfiTecture for livE video stReaming (RICHTER). We first introduce RICHTER's multi-layer architecture and design an action tree that considers all feasible resources provided by peers, edge, and CDN servers for serving peer requests with minimum latency and maximum quality. We then formulate the problem as an optimization model executed at the edge of the network. We present an Online Learning (OL) approach that leverages an unsupervised Self Organizing Map (SOM) to (i) alleviate the time complexity issue of the optimization model and (ii) make it a suitable solution for large-scale scenarios, by enabling decisions for groups of requests instead of for single requests. Finally, we implement the RICHTER framework, conduct our experiments on a large-scale cloud-based testbed including 350 HAS players, and compare its effectiveness with baseline systems. The experimental results illustrate that RICHTER outperforms baseline schemes in terms of users' Quality of Experience (QoE), latency, and network utilization, by at least 59%, 39%, and 70% respectively.

Fog computing represents an extension of the Cloud infrastructure, which allows the improvement of the performance of IoT applications. The problem of task scheduling represents a challenge in this type of environment, with the aim of how to allocate the tasks to the different nodes of the Fog-Cloud infrastructure, in order to minimize makespan, cost, response time, and energy. In this paper, we propose SQGA— an algorithm to improve the workflow scheduling in Fog-Cloud environment. This algorithm is based on the quantum genetic algorithm QGA and aims to improve the makespan of applications deployed in the Fog-Cloud computing environment. The proposed SQGA scheduling algorithm is compared to the classical genetic algorithm and the First Come First Served algorithm. The experiment results show that the proposed SQGA algorithm is more efficient in makespan, and adapts better to the available resources.

Low latency live streaming over HTTP using Dynamic Adaptive Streaming over HTTP (LL-DASH) and HTTP Live Streaming (LL- HLS) has emerged as a new way to deliver live content with an respectable video quality and short end-to-end latency. Satisfying these requirements while maintaining viewer experience in practice is challenging, and adopting conventional adaptive bitrate (ABR) schemes directly to do so will not work. Therefore, recent solutions including LoL+, L2A, Stallion, and Llama re-think conventional ABR schemes to support low-latency scenarios. These solutions have been integrated with dash.js [9] that supports LL-DASH. However, their performance in LL-HLS remains in question. To bridge this gap, we implement and integrate existing LL-DASH ABR schemes in the hls.js video player [18] which supports LL-HLS. Moreover, a series of real-world trace-driven experiments have been conducted to check their efficiency under various network conditions including a comparison with results achieved for LL-DASH in dash.js. Our version of hls.js is publicly available at [3] and a demo at [4].

Our earlier Low-on-Latency (dubbed as LoL) solution offered an accurate bandwidth prediction and rate adaptation algorithm tailored for live streaming applications that targeted an end-to-end latency of up to two seconds. While LoL was a significant step forward in multi-bitrate low-latency live streaming, further experimentation and testing showed that there was room for improvement in three areas. First, LoL used hard-coded parameters computed from an offline training process in the rate adaptation algorithm and this was seen as a significant barrier in LoL’s wide deployment. Second, LoL’s objective was to maximize a collective QoE function. Yet, certain use cases have specific objectives besides the singular QoE and this had to be accommodated. Third, the adaptive playback speed control failed to produce satisfying results in some scenarios. Our goal in this paper is to address these areas and make LoL sufficiently robust to deploy. We refer to the enhanced solution as LoL$^+$, which has been integrated to the official dash.js player in v3.2.0.

Recent years have seen tremendous growth in HTTP adaptive live video traffic over the Internet. In the presence of highly dynamic network conditions and diverse request patterns, existing yet simple hand-crafted heuristic approaches for serving client requests at the network edge might incur a large overhead and significant increase in time complexity. Therefore, these approaches might fail in delivering acceptable Quality of Experience (QoE) to end users. To bridge this gap, we propose ROPL, a learning-based client request management solution at the edge that leverages the power of the recent breakthroughs in deep reinforcement learning, to serve requests of concurrent users joining various HTTP-based live video channels. ROPL is able to react quickly to any changes in the environment, performing accurate decisions to serve clients requests, which results in achieving satisfactory user QoE. We validate the efficiency of ROPL through trace-driven simulations and a real-world setup. Experimental results from real-world scenarios confirm that ROPL outperforms existing heuristic-based approaches in terms of QoE, with a factor up to$3.7\times$.

Recently, the advancements in technologies have enabled volumetric media techniques to capture, encode, decode, and render videos in six degree-of-freedom (6DoF) in order to make the objects highly immersive, interactive, and expressive within the scene. This is enabled by using multiple cameras around the object(s). However, streaming 6DoF videos require a huge bandwidth and computational processing. As the end-user focuses on viewport-scenes, a large portion of the consumed bandwidth is mainly introduced due to unseen video-scenes. To fill this gap, it is imperative to predict the future head-movement (future viewport) of end-user in order to avoid the waste of network bandwidth and reduce computational processing power. In this paper, we propose a holistic architecture for the future viewport prediction using deep-neural-network (DNN)-based model. Specifically, our solution uses residual long-short-term-memory (RLSTM) architecture for accurate future viewport prediction. We confirm the effectiveness of our solution through trace-driven streaming experiments using a popular public dataset over four categories of DNN models: linear, dense, convolutional, and long-short-term-memory (LSTM). Experimental results show that our solution is able to achieve the lowest possible mean absolute error of ~ 0.01 compared to its competitor.

HTTP adaptive streaming (HAS) has become the de-facto standard for delivering video over the Internet. More content providers like YouTube and Twitch have started generating and delivering high quality streams (usually 4k resolution) with advanced end-to-end encryption mechanisms. This huge increase in HAS encrypted traffic, creates a significant challenge for network providers in understanding what is happening on their infrastructures which limits their ability to manage network infrastructures properly. Due to such invisibility, the network providers could not take appropriate decisions for better optimizations, resulting in significant revenue lost. Inferring the quality of experience (QoE) of HAS-based streaming video services is important, but recent studies highlight that most of existing solutions that rely on packet inspections, showing low performance in inference accuracy. To address this issue, we develop a machine learning powered system that infers QoE factors such as startup delay, rebuffering and selected quality, for encrypted on-demand HAS streaming video services. Our solution uses two data-driven techniques: Deep Self Organizing Map (DSOM) and Multi Layer Perceptron Backpropagation (MLPB), allowing efficient accuracy with low error in inferring QoE factors over several public video datasets, compared to some state-of-the-art approaches.

Live User Generated Content (UGC) has become very popular in today’s video streaming applications, in particular with gaming and e-sport. However, streaming UGC presents unique challenges for video delivery. When dealing with the technical complexity of managing hundreds or thousands of concurrent streams that are geographically distributed, UGC systems are forces to made difficult trade-offs with video quality and latency. To bridge this gap, this paper presents a fully distributed architecture for UGC delivery over the Internet, termed QuaLA (joint Quality-Latency Architecture). The proposed architecture aims to jointly optimize video quality and latency for a better user experience and fairness. By using the proximal Jacobi alternating direction method of multipliers (ProxJ-ADMM) technique, QuaLA proposes a fully distributed mechanism to achieve an appropriate solution. We demonstrate the effectiveness of the proposed architecture through real-world experiments using the CloudLAB testbed. Experimental results show the outperformance of QuaLA in achieving high quality with more than 57% improvement while preserving a good level of fairness and respecting a given target latency among all clients compared to conventional client-driven solutions.

More users have a growing interest in low latency over-the-top (OTT) applications such as online video gaming, video chat, online casino, sports betting, and live auctions. OTT applications face challenges in delivering low latency live streams using Dynamic Adaptive Streaming over HTTP (DASH) due to large playback buffer and video segment duration. A potential solution to this issue is the use of HTTP chunked transfer encoding (CTE) with the common media application format (CMAF). This combination allows the delivery of each segment in several chunks to the client, starting before the segment is fully available in real-time. However, CTE and CMAF alone are not sufficient as they do not address other limitations and challenges at the client-side, including inaccurate bandwidth measurement, latency control, and bitrate selection.

In September 2020, the Consumer Technology Association (CTA) published the CTA-5004: Common Media Client Data (CMCD) specification. Using this specification, a media client can convey certain information to the content delivery network servers with object requests. This information is useful in log association/analysis, quality of service/experience monitoring and delivery enhancements. This paper is the first step toward investigating the feasibility of CMCD in addressing one of the most common problems in the streaming domain: efficient use of shared bandwidth by multiple clients. To that effect, we implemented CMCD functions on an HTTP server and built a proof-of-concept system with CMCD-aware dash.js clients. We show that even a basic bandwidth allocation scheme enabled by CMCD reduces rebuffering rate and duration without noticeably sacrificing the video quality.

Adaptive bitrate (ABR) algorithms play a crucial role in delivering the highest possible viewer's Quality of Experience (QoE) in HTTP Adaptive Streaming (HAS). Online video streaming service providers use HAS - the dominant video streaming technique on the Internet - to deliver the best QoE for their users. A viewer's delight relies heavily on how the ABR of a media player can adapt the stream's quality to the current network conditions. QoE for video streaming sessions has been assessed in many research projects to give better insight into the significant quality metrics such as startup delay and stall events. The ITU Telecommunication Standardization Sector (ITU-T) P.1203 quality evaluation model allows to algorithmically predict a subjective Mean Opinion Score (MOS) by considering various quality metrics. Subjective evaluation is the best assessment method for examining the end-user opinion over a video streaming session's experienced quality. We have conducted subjective evaluations with crowdsourced participants and evaluated the MOS of the sessions using the ITU-T P.1203 quality model. This paper's main contribution is to investigate the correspondence of subjective and objective evaluations for well-known heuristic-based ABRs.

Today's HTTP adaptive streaming solutions use a variety of algorithms to measure the available network bandwidth and predict its future values. Bandwidth prediction, which is already a difficult task, must be more accurate when lower latency is desired due to the shorter time available to react to bandwidth changes, and when mobile networks are involved due to their inherently more frequent and potentially larger bandwidth fluctuations. Any inaccuracy in bandwidth prediction results in flawed adaptation decisions, which will in turn translate into a diminished viewer experience. We propose an Automated Model for Prediction (AMP) that encompasses techniques for bandwidth prediction and model auto-selection specifically designed for low-latency live steaming with chunked transfer encoding. We first study statistical and computational intelligence techniques to implement a suite of bandwidth prediction models that can work accurately under a broad range of network conditions, and second, we introduce an automated prediction model selection method. We confirm the effectiveness of our solution through trace-driven live streaming experiments.

Nowadays, Internet traffic encryption is rapidly increasing due to privacy and security concerns. This is because of the massive usage of end-to-end security protocols over Internet such as HTTPS and QUIC. The encryption trend will continue to rapidly increase in the future, and this trend concerns video streaming applications as well. Network providers face a serious challenge in managing their networks due to such widespread deployment of end-to-end security protocols. These operators need to have a clear visibility into traffic on their networks to monitor and manage both quality of experience (QoE)-and-service (QoS) impairments in popular video streaming services, in the most effective and efficient manner. Moreover, so many factors that influence QoE need to be taken care of to get an acceptable user experience. Most of the existing solutions use the deep packet inspection to infer these factors from the encrypted traffic. However, these solutions are inefficient, most of the time, leading to low QoE inference accuracy. To bridge this gap, we propose a machine-learning based solution that leverages a random forest classifier for a better QoE inference accuracy. The proposed solution uses network-and-transport layer information to infer QoE factors such as startup delay and stall events. It helps the network providers to react quickly and in real time for any impairments in the QoE of the encrypted video traffic. We evaluate our solution using an HTTP adaptive streaming service (YouTube) that uses HTTPS and QUIC protocols. Our experimental results show that our solution achieves up to 91.1% classification accuracy for HTTPS and up to 87.3% for QUIC.

Live streaming remains a challenge in the adaptive streaming space due to the stringent requirements for not just quality and rebuffering, but also latency. Many solutions have been proposed to tackle streaming in general, but only few have looked into better catering to the more challenging low-latency live streaming scenarios. In this paper, we re-visit and extend several important components (collectively called Low-on-Latency, LoL) in adaptive streaming systems to enhance the low-latency performance. LoL includes bitrate adaptation (both heuristic and learning-based), playback control and throughput measurement modules.

HTTP adaptive streaming with chunked transfer encoding can offer low-latency streaming without sacrificing the coding efficiency. This allows media segments to be delivered while still being packaged. However, conventional schemes often make widely inaccurate bandwidth measurements due to the presence of idle periods between the chunks and hence this is causing sub-optimal adaptation decisions. To address this issue, we earlier proposed ACTE (ABR for Chunked Transfer Encoding) [6], a bandwidth prediction scheme for low-latency chunked streaming. While ACTE was a significant step forward, in this study we focus on two still remaining open areas, namely, (i) quantifying the impact of encoding parameters, including chunk and segment durations, bitrate levels, minimum interval between IDR-frames and frame rate on ACTE, and (ii) exploring the impact of video content complexity on ACTE. We thoroughly investigate these questions and report on our findings. We also discuss some additional issues that arise in the context of pursuing very low latency HTTP video streaming.

The significant popularity of HTTP adaptive video streaming (HAS), such as Dynamic Adaptive Streaming over HTTP (DASH), over the Internet has led to a stark increase in user expectations in terms of video quality and delivery robustness. This situation creates new challenges for content providers who must satisfy the Quality-of-Experience (QoE) requirements and demands of their customers over a best-effort network infrastructure. Unlike traditional single server DASH, we developed a D istributed Q ueuing theory bitrate adaptation algorithm for DASH (DQ-DASH) that leverages the availability of multiple servers by downloading segments in parallel. DQ-DASH uses a M x /D/1/K queuing theory based bitrate selection in conjunction with the request scheduler to download subsequent segments of the same quality through parallel requests to reduce quality fluctuations. DQ-DASH facilitates the aggregation of bandwidth from different servers and increases fault-tolerance and robustness through path diversity. The resulting resilience prevents clients from suffering QoE degradations when some of the servers become congested. DQ-DASH also helps to fully utilize the aggregate bandwidth from the servers and download the imminently required segment from the server with the highest throughput. We have also analyzed the effect of buffer capacity and segment duration for multi-source video streaming.

The majority of Internet video traffic today is delivered via HTTP Adaptive Streaming (HAS). Recent studies concluded that pure client-driven HAS adaptation is likely to be sub-optimal, given clients adjust quality based on local feedback. In [1], we introduced a network-assisted streaming architecture (BBGDASH) that provides bounded bitrate guidance for a video client while preserving quality control and adaptation at the client. Although BBGDASH is an efficient approach for video delivery, deploying it in a wireless network environment could result in sub-optimal decisions due to the high fluctuations. To this end, we propose in this paper an intelligent streaming architecture (denoted BBGDASH+), which leverages the power of time series forecasting to allow for an accurate and scalable networkbased guidance. Further, we conduct an initial investigation of parameter settings for the forecasting algorithms in a wireless testbed. Overall, the experimental results indicate the potential of the proposed approach to improve video delivery in wireless network conditions.

Live video streaming delivery over Dynamic Adaptive Video Streaming (DASH) is challenging as it requires low end-to-end latency, is more prone to stall, and the receiver has to decide online which representation at which bitrate to download and whether to adjust the playback speed to control the latency. To encourage the research community to come together to address this challenge, we organize the Live Video Streaming Grand Challenge at ACM Multimedia 2019. This grand challenge provides a simulation platform onto which the participants can implement their adaptive bitrate (ABR) logic and latency control algorithm, and then benchmark against each other using a common set of video traces and network traces. The ABR algorithms are evaluated using a common Quality-of- Experience (QoE) model that accounts for playback bitrate, latency constraint, frame-skipping penalty, and rebuffering penalty.

HTTP adaptive streaming with chunked transfer encoding can be used to offer low-latency streaming without sacrificing the coding efficiency. While this allows a media segment to be generated and delivered at the same time, which is critical in reducing the latency, the conventional bitrate adaptation schemes make often grossly inaccurate bandwidth measurements due to the presence of idle periods between the chunks. These wrong measurements cause the streaming client to make bad adaptation decisions. To this end, we design ACTE, a new bitrate adaptation scheme that leverages the unique nature of chunk downloads. ACTE uses a sliding window to accurately measure the available bandwidth and an online linear adaptive filter to predict the bandwidth into the future. Results show that ACTE achieves 96% measurement accuracy, which translates to a 65% reduction in the number of stalls and a 49% increase in quality of experience on average compared to other schemes.

The dramatic growth of HTTP adaptive streaming (HAS) traffic represents a practical challenge for service providers in satisfying the demand from their customers. Achieving this in a network where multiple players share the network capacity has so far proved hard because of the bandwidth competition among the HAS players. This competition is exacerbated by the bandwidth overestimation that is introduced due to the isolated and selfish behavior of the HAS players. Each player strives individually to select the maximum bitrate without considering the co-existing players or network resource dynamics. As a result, the HAS players suffer from video quality instability, quality unfairness, and network underutilization or oversubscription, and the players observe a poor quality of experience (QoE). To address this issue, we propose a fully distributed game theory and consensus-based collaborative adaptive bitrate solution for shared network environments, termed Game Theory and consensus-based Approach for Cooperative HAS delivery systems (GTAC). Our solution consists of two-stage games that run in parallel during a streaming session. We extensively evaluate GTAC on a broad set of trace-driven and real-world experiments. Results show that GTAC enhances the viewer QoE by up to 22%, presentation quality stability by up to 24%, fairness by at least 31%, and network utilization by 28% compared to the well-known schemes.

Past research has shown that concurrent HTTP adaptive streaming (HAS) players behave selfishly and the resulting competition for shared resources leads to underutilization or oversubscription of the network, presentation quality instability and unfairness among the players, all of which adversely impact the viewer experience. While coordination among the players, as opposed to all being selfish, has its merits and may alleviate some of these issues. A fully distributed architecture is still desirable in many deployments and better reflects the design spirit of HAS. In this study, we focus on and propose a distributed bitrate adaptation scheme for HAS that borrows ideas from consensus and game theory frameworks. Experimental results show that the proposed distributed approach provides significant improvements in terms of viewer experience, presentation quality stability, fairness and network utilization, without using any explicit communication between the players.

In streaming media, it is imperative to deliver a good viewer experience to preserve customer loyalty. Prior research has shown that this is rather difficult when shared Internet resources struggle to meet the demand from streaming clients that are largely designed to behave in their own self-interest. To date, several schemes for adaptive streaming have been proposed to address this challenge with varying success. In this paper, we take a different approach and develop a game theoretic approach. We present a practical implementation integrated in the dash.js reference player and provide substantial comparisons against the state-of-the-art methods using trace-driven and real-world experiments. Our approach outperforms its competitors in the average viewer experience by 38.5% and in video stability by 62%.

In designing an HTTP adaptive streaming (HAS) system, the bitrate adaptation scheme in the player is a key component to ensure a good quality of experience (QoE) for viewers. We propose a new online reinforcement learning optimization framework, called ORL-SDN, targeting HAS players running in a software-defined networking (SDN) environment. We leverage SDN to facilitate the orchestration of the adaptation schemes for a set of HAS players. To reach a good level of QoE fairness in a large population of players, we cluster them based on a perceptual quality index. We formulate the adaptation process as a Partially Observable Markov Decision Process and solve the per-cluster optimization problem using an online Q-learning technique that leverages model predictive control and parallelism via aggregation to avoid a per-cluster suboptimal selection and to accelerate the convergence to an optimum. This framework achieves maximum long-term revenue by selecting the optimal representation for each cluster under time-varying network conditions. The results show that ORL-SDN delivers substantial improvements in viewer QoE, presentation quality stability, fairness, and bandwidth utilization over well-known adaptation schemes.

HTTP adaptive streaming (HAS) is receiving much attention from both industry and academia as it has become the de facto approach to stream media content over the Internet. Recently, we proposed a streaming architecture called SDNDASH [1] to address HAS scalability issues including video instability, quality of experience (QoE) unfairness, and network resource underutilization, while maximizing per player QoE. While SDNDASH was a significant step forward, there were three unresolved limitations: 1) it did not scale well when the number of HAS players increased; 2) it generated communication overhead; and 3) it did not address client heterogeneity. These limitations could result in suboptimal decisions that led to viewer dissatisfaction. To that effect, we propose an enhanced intelligent streaming architecture, called SDNHAS, which leverages software defined networking (SDN) capabilities of assisting HAS players in making better adaptation decisions. This architecture accommodates large-scale deployments through a cluster-based mechanism, reduces communication overhead between the HAS players and SDN core, and allocates the network resources effectively in the presence of short- and long-term changes in the network.

HTTP adaptive streaming (HAS) is being adopted with increasing frequency and becoming the de-facto standard for video streaming. However, the client-driven, on-off adaptation behavior of HAS results in uneven bandwidth competition and this is exacerbated when a large number of clients share the same bottleneck network link and compete for the available bandwidth. With HAS each client independently strives to maximize its individual share of the available bandwidth, which leads to bandwidth competition and a decrease in end-user quality of experience (QoE). The competition causes scalability issues, which are quality instability, unfair bandwidth sharing and network resource underutilization. We propose a new software defined networking (SDN) based dynamic resource allocation and management architecture for HAS systems, which aims to alleviate these scalability issues and improve the per-client QoE. Our architecture manages and allocates the network resources dynamically for each client based on its expected QoE. Experimental results show that the proposed architecture significantly enhances scalability by improving per-client QoE by at least 30% and supporting up to 80% more clients with the same QoE compared to the conventional schemes.

Mobile Ad hoc networks (MANETs) are self-organizing and self-configuring multi-hop wireless networks without any pre-existing communication infrastructures or centralized management. Scalability in MANETs is a new issue where network topology includes large number of nodes and demands a large number of packets in limited wireless bandwidth and nodes mobility that results in a high frequency of failure regarding wireless links. Clustering in MANETs is an important topic that divides the large network into several sub networks and widely used in efficient network management, improving resource management, hierarchical routing protocol design, Quality of Service and a good monitoring architecture of MANETs security. Subsequently, many clustering approaches have been proposed to divide nodes into clusters to support routing and network management. In this paper, we propose a new efficient weight based clustering algorithm. It takes into consideration the metrics: trust (T), density (D), Mobility (M) and energy (E) to choose locally the optimal cluster heads during cluster formation phase. In our proposed algorithm each cluster is supervised by its cluster head in order to ensure an acceptable level of security. It aims to improve the usage of scarce resources such as bandwidth, maintaining stable clusters structure with a lowest number of clusters formed, decreasing the total overhead during cluster formation and maintenance, maximizing lifespan of mobile nodes in the network and reduces energy consumption. Preliminary simulation experiments are conducted to compare the performance of our algorithm to Lowest ID, Highest Degree and WCA in terms of Average Number of CHs, Average Number of CH Changes, Total Number of Re-affiliations, Clusters Stability and Total Overhead. The initial results show that our scheme performs better than other clustering schemes based on the performance metrics considered.