Adaptive Streaming Solution Research Articles

FReD-ViQ: Fuzzy Reinforcement Learning Driven Adaptive Streaming Solution for Improved Video Quality of Experience

FReD-ViQ: Fuzzy Reinforcement Learning Driven Adaptive Streaming Solution for Improved Video Quality of Experience

MEDUSA: A Dynamic Codec Switching Approach in HTTP Adaptive Streaming

HTTP Adaptive Streaming (HAS) solutions utilize various Adaptive BitRate (ABR) algorithms to dynamically select appropriate video representations, aiming to adapt to fluctuations in network bandwidth. However, current ABR implementations have a limitation in that they are designed to function with one set of video representations, i.e. , the bitrate ladder, which differ in bitrate and resolution, but are encoded with the same video codec. When multiple codecs are available, current ABR algorithms select one of them prior to the streaming session and stick to it throughout the entire streaming session. Although newer codecs are generally preferred over older ones, their compression efficiencies differ depending on the content’s complexity, which varies over time. Therefore, it is necessary to select the appropriate codec for each video segment to reduce the requested data while delivering the highest possible quality. In this paper, we first provide a practical example where we compare compression efficiencies of different codecs on a set of video sequences. Based on this analysis, we formulate the optimization problem of selecting the appropriate codec for each user and video segment (on a per-segment basis in the outmost case), refining the selection of the ABR algorithms by exploiting key metrics, such as the perceived segment quality and size. Subsequently, to address the scalability issues of this centralized model, we introduce a novel distributed plug-in ABR algorithm for Video on Demand (VoD) applications called MEDUSA to be deployed on top of existing ABR algorithms. MEDUSA enhances the user’s Quality of Experience (QoE) by utilizing a multi-objective function that considers the quality and size of video segments when selecting the next representation. Using quality information and segment size from the modified Media Presentation Description (MPD) , MEDUSA utilizes buffer occupancy to prioritize quality or size by assigning specific weights in the objective function. To show the impact of MEDUSA, we compare the proposed plug-in approach on top of state-of-the-art techniques with their original implementations and analyze the results for different network traces, video content, and buffer capacities. According to the experimental findings, MEDUSA shows the ability to improve QoE for various test videos and scenarios. The results reveal an impressive improvement in the QoE score of up to 42% according to the ITU-T P.1203 model (mode 0). Additionally, MEDUSA can reduce the transmitted data volume by up to more than 40% achieving a QoE similar to the techniques compared, reducing the burden on streaming service providers for delivery costs.

Adaptive video streaming solution based on multi-access edge computing advantages

Adaptive video streaming solution based on multi-access edge computing advantages

DoFP+: An HTTP/3-Based Adaptive Bitrate Approach Using Retransmission Techniques

HTTP Adaptive Streaming (HAS) solutions use various adaptive bitrate (ABR) algorithms to select suitable video qualities with the objective of coping with the variations of network connections. HTTP has been evolving with various versions and provides more and more features. Most of the existing ABR algorithms do not significantly benefit from the HTTP development when they are merely supported by the most recent HTTP version. An open research question is “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">How can new features of the recent HTTP versions be used to enhance the performance of HAS</i> ?” To address this question, in this paper, we introduce Days of Future Past+ (DoFP+ for short), a heuristic algorithm that takes advantage of the features of the latest HTTP version, HTTP/3, to provide high Quality of Experience (QoE) to the viewers. DoFP+ leverages HTTP/3 features, including (i) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">stream multiplexing</i> , (ii) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">stream priority</i> , and (iii) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">request cancellation</i> to upgrade low-quality segments in the player buffer while downloading the next segment. The qualities of those segments are selected based on an objective function and throughput constraints. The objective function takes into account two factors, namely the (i) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">average bitrate</i> and the (ii) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">video instability</i> of the considered set of segments. We also examine different strategies of download order for those segments to optimize the QoE in limited resources scenarios. The experimental results show an improvement in QoE by up to 33% while the number of stalls and stall duration for DoFP+ are reduced by 86% and 92%, respectively, compared to state-of-the-art ABR schemes. In addition, DoFP+ saves on average up to 16% downloaded data across all test videos. Also, we find that downloading segments sequentially brings more benefits for retransmissions than concurrent downloads; and lower-quality segments should be upgraded before other segments to gain more QoE improvement. Our source code has been published for reproducibility at https://github.com/cd-athena/ DoFP-Plus.

A Combined Field-of-View Prediction-Assisted Viewport Adaptive Delivery Scheme for 360° Videos

Recently, 360° or omnidirectional videos have become increasingly popular for both personal and enterprize use-cases. However, 360° video streaming has very high bandwidth and processing requirements. State-of-the-art viewport-based streaming solutions lower these requirements by performing selective streaming based on long-term Field-of-View (FoV) prediction mechanisms. However, sometimes user movement is extremely unpredictable during some parts of the video, and applying these solutions adversely affects the overall quality of experience (QoE). This paper proposes a novel Combined Field-of-View tile-based adaptive streaming solution (CFOV) that improves end-user QoE for 360° video streaming. CFOV performs interactive tile selection based on more accurate dynamical viewing area identification by combining the results of two FoV prediction mechanisms. It also employs an innovative priority-based bitrate adaptation approach that ensures improved bitrate budget distribution between different tiles. We evaluate the proposed solution with a comprehensive set of experiments involving four immersive videos, diverse tiling patterns (i.e., 4×3, 6×4, and 8×6), different segment lengths (i.e., 1s, 2s, and 3s), and 48 empirical head movement traces under different bandwidth settings. The evaluation employs a newly defined QoE metric specifically introduced to assess the streaming performance of 360° videos objectively. The experimental findings show that, compared to alternative approaches, our proposed solution can achieve a higher viewport match and can significantly improve the user QoE for different watching behaviors and content characteristics.

Video Super-Resolution and Caching—An Edge-Assisted Adaptive Video Streaming Solution

Edge computing provides the potential to improve users&#x2019; Quality of Experience (QoE) in ever-increasing video delivery. However, existing edge-based solutions cannot fully utilize the edge computing power and storage capacity. This paper proposes <b>VI</b>deo <b>S</b>uper-resolution and <b>CA</b>ching (VISCA), an edge-assisted adaptive video streaming solution, which integrates super-resolution and edge caching to improve users&#x2019; QoE. We design a novel edge-based ABR algorithm that makes bitrate and video chunk source decisions by considering network conditions, QoE objectives, and edge resource availability jointly. VISCA utilizes super-resolution to enhance the cached low-quality video at the edge. The super-resolution models used are trained for the most popular videos only in order to achieve quality improvements with a fraction of the computation. A novel cache strategy is also adopted to maximize caching efficiency. To assess the performance of VISCA, an implemented prototype of VISCA was deployed in synthetic and real network contexts. Compared with the existing video streaming solutions, VISCA improves video quality by 28.2&#x0025;-251.2&#x0025; and reduces rebuffering time by 16.1&#x0025;-95.6&#x0025; in all considered scenarios.

Multipath Dynamic Adaptive Streaming over HTTP Using Scalable Video Coding in Software Defined Networking

Dynamic Adaptive Streaming over HTTP (DASH) offers adaptive and dynamic multimedia streaming solutions to heterogeneous end systems. However, it still faces many challenges in determining an appropriate rate adaptation technique to provide the best quality of experience (QoE) to the end systems. Most of the suggested approaches rely on servers or client-side heuristics to improve multimedia streaming QoE. Moreover, using evolving technologies such as Software Defined Networking (SDN) that provide a network overview, combined with Multipath Transmission Control Protocol (MPTCP), can enhance the QoE of streaming multimedia media based on scalable video coding (SVC). Therefore, we enhance our previous work and propose a Dynamic Multi Path Finder (DMPF) scheduler that determines optimal techniques to enhance QoE. DMPF scheduler is a part of the DMPF Scheduler Module (DSM) which runs as an application over the SDN controller. The DMPF scheduler accommodates maximum client requests while providing the basic representation of the media requested. We evaluate our implementation on real network topology and explore how SVC layers should be transferred over network topology. We also test the scheduler for network bandwidth usage. Through extensive simulations, we show clear trade-offs between the number of accommodated requests and the quality of the streaming. We conclude that it is better to schedule the layers of a request into the same path as much as possible than into multiple paths. Furthermore, these result would help service providers optimize their services.

Data-Driven Bandwidth Prediction Models and Automated Model Selection for Low Latency

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.

QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey

We provide in this paper a tutorial and a comprehensive survey of QoE management solutions in current and future networks. We start with a high level description of QoE management for multimedia services, which integrates QoE modelling, monitoring, and optimization. This followed by a discussion of HTTP Adaptive Streaming (HAS) solutions as the dominant technique for streaming videos over the best-effort Internet. We then summarize the key elements in SDN/NFV along with an overview of ongoing research projects, standardization activities and use cases related to SDN, NFV, and other emerging applications. We provide a survey of the state-of-the-art of QoE management techniques categorized into three different groups: a) QoE-aware/driven strategies using SDN and/or NFV; b) QoE-aware/driven approaches for adaptive streaming over emerging architectures such as multi-access edge computing, cloud/fog computing, and information-centric networking; and c) extended QoE management approaches in new domains such as immersive augmented and virtual reality, mulsemedia and video gaming applications. Based on the review, we present a list of identified future QoE management challenges regarding emerging multimedia applications, network management and orchestration, network slicing and collaborative service management in softwarized networks. Finally, we provide a discussion on future research directions with a focus on emerging research areas in QoE management, such as QoE-oriented business models, QoE-based big data strategies, and scalability issues in QoE optimization.

Muslin: A QoE‐aware CDN resources provisioning and advertising system for cost‐efficient multisource live streaming

SummaryDelivering video content with a high and fairly shared quality of experience is a challenging task in view of the drastic video traffic increase forecasts, as live video traffic will grow 15‐fold by 2022. Currently, content delivery networks provide numerous servers hosting replicas of the video content, and consuming clients are redirected to the closest server. Then, the video content is streamed using adaptive streaming solutions. However, servers and network links often become overloaded during major events, and users may experience a poor or unfairly distributed quality of experience, unless more servers are provisioned. In this paper, we propose Muslin, a streaming solution supporting a high, fairly shared end users' quality of experience for live streaming, while minimizing the required content delivery platform scale. Muslin leverages on MS‐Stream, a content delivery solution, which aggregates video content from multiple servers to offer a high quality of experience for its users. Muslin dynamically provisions servers and replicates content into servers and advertises servers to clients based on real‐time delivery conditions. We have used Muslin to replay a 1‐day video‐games event, with hundreds of clients and several test beds. Our results show that our approach outperforms traditional content delivery schemes by increasing the fairness and quality of experience at the user side with a smaller infrastructure scale.

Measuring bandwidth and buffer occupancy to improve the QoE of HTTP adaptive streaming

Live and on-demand video streaming service systems consume significant portion of Internet traffic all over the world. HTTP adaptive streaming is becoming the de-facto standard for adaptive video streaming solutions. Conventional estimation scheme for bandwidth estimation is not appropriate to estimate the bandwidth when multiple clients compete for a common bottleneck link, due to the ON–OFF traffic pattern. They overestimate the network bandwidth which leads to degradation of quality of experience by unnecessary changes in video quality, average video quality and unfairness of video quality. In this paper, we proposed receiver-side bandwidth-measured method to achieve a better quality of experience in multiple-client scenario. The proposed method estimates the obtainable network bandwidth based on the buffer status and segment throughput. The video buffer model is associated with three thresholds (i.e., one for initial start-up and two for operating thresholds). NS-3 network simulator has been deployed to measure the performance of HTTP adaptive streaming. Simulation outputs reflect that the proposed method enhances the quality of experience than conventional methods.

PMS

Single-source HTTP adaptive streaming solutions (HAS) have become the de facto solutions to deliver live video over the Internet. By avoiding video stalling events that are mainly caused by the lack of throughput at client or at server side, HAS solutions increase the end users’ quality of experience (QoE). We propose to pragmatically extend HAS with our MS-Stream solution that simultaneously utilizes several servers. MS-Stream aims at offering high QoE for live content delivery by exploiting expanded bandwidth and link diversity in distributed heterogeneous infrastructures. By leveraging end users’ connectivity capacities, we further extend the QoE and scalability capabilities of our proposal by exposing a hybrid P2P/multisource live-streaming solution (P2P/MS-Stream (PMS)), achieving trade-offs between the system’s scale and the end users’ QoE. We propose a distributed quality adaptation algorithm run by every peer, along with a local optimization method of the usage of the server infrastructure made available. Large-scale evaluations conducted with 300 peers located in France permits validating our approach and algorithms over flash crowd events and allow us to conclude that PMS can reach the optimal trade-offs between QoE and system scale.

Optimal Representations for Adaptive Streaming in Interactive Multiview Video Systems

Interactive multiview video streaming (IMVS) services permit to remotely navigate within a 3D scene with an immersive experience. This is possible by transmitting a set of reference camera views (anchor views), which are used by the clients to freely navigate in the scene and possibly synthesize additional viewpoints of interest. From a networking perspective, the big challenge in IMVS systems is to deliver to each client the best set of anchor views that maximizes the navigation quality, minimizes the view-switching delay and yet satisfies the network constraints. Integrating adaptive streaming solutions in free-viewpoint systems offers a promising solution to deploy IMVS in large and heterogeneous scenarios, as long as the multiview video representations on the server are properly selected. Therefore, we propose to optimize the multiview data at the server by minimizing the overall resource requirements while offering a good navigation quality to the different users. We propose a representation set optimization problem for multiview adaptive streaming systems, and we show that it is NP-hard. Therefore, we introduce the concept of multiview navigation segment that permits to cast the video representation set selection as an integer linear programming problem with a bounded computational complexity. We then show that the proposed solution reduces the computational complexity, while preserving optimality in most of the 3D scenes. We finally provide simulation results for different classes of users and show the gain offered by an optimal multiview video representation selection compared to recommended representation sets (e.g., Netflix and Apple ones) or to a baseline representation selection algorithm, where the encoding parameters are decided a priori for all the camera views.

Improving QoE and Fairness in HTTP Adaptive Streaming Over LTE Network

HTTP adaptive streaming (HAS) has emerged as the main technology for video streaming applications. Multiple HAS video clients sharing the same wireless channel may experience different video qualities as well as different play-out buffer levels, as a result of both different video content complexities and different channel conditions. This causes unfairness in the end-user quality of experience. In this paper, we propose a quality-fair adaptive streaming solution with fair buffer (QFAS-FB) to deliver fair video quality and to achieve asymptotically fair play-out buffer levels among HAS clients competing for the same wireless resources in a long-term evolution (LTE) cell. In the QFAS-FB framework, the share of radio resources is optimized according to video content characteristics, play-out buffer levels, and channel conditions. The proposed solution is compared with the other state-of-the-art strategies, and the numerical results show that it significantly improves the quality fairness among heterogeneous HAS users, reduces the video quality variations, and improves the fairness among the user’s play-out buffers.

Cooperative mobile video transmission for traffic surveillance in smart cities

Cooperation transmission among mobile devices is critical for video streaming systems. Conventional systems usually require huge storage space and bandwidth, subsequently causing a bottleneck for the development of streaming systems. To resolve such problems, this study proposes a cooperative mechanism for mobiles that can make full use of local storage space and uploading bandwidth to relieve the computational load for streaming servers. Firstly, a statistical distribution of the life cycle of devices is dynamically established based on the analysis of random arrival and departure of mobile users. Secondly, an adaptive sliding-window technology based on Scalable Video Coding (SVC) is proposed in this study to change the size of windows in real time. Thirdly, an adaptive streaming solution is introduced in the OPNET environment, which cooperatively integrates the strategies of overlay formation, quality adaptation, and data scheduling. Simulation results showed that the proposed mechanism saved power consumption of devices and ensured the video quality for networks with limited resources.

TCP based-user control for adaptive video streaming

Nowadays, Media streaming services over TCP have become very popular because of the TCP's reliability, which provides remarkable stability to the Internet. However, in order to offer a high media quality and a good user satisfaction, the media streaming service requires that transport protocols can be adapted continuously with the network parameters. However, the diversity, of terminals (i.e., tablet, smart phones, laptop ź etc.) and their corresponding capabilities, means that users' agnostic solutions are inefficient to cope with such diverse contexts. Indeed, the intrinsic characteristics and parameters of the terminal users (i.e., devices) need to be taken into account on the video streaming adaptation process. The classic adaptive video streaming services do not consider the user parameters on the adaptation process. In this paper, we propose an adaptive video streaming solution to improve the user satisfaction factor by adapting the TCP parameters according to the user's parameters on mobile networks. The user satisfaction factor is calculated according to some metrics driven from the user's quality of experience (QoE). The work is validated through our proposal based on a new mobile agent (which does all the work) developed on a Linux script platform and tested on different kinds of devices with different scenarios.

QoE-driven in-network optimization for Adaptive Video Streaming based on packet sampling measurements

HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for adaptive streaming solutions. In HAS, a video is temporally split into segments which are encoded at different quality rates. The client can then autonomously decide, based on the current buffer filling and network conditions, which quality representation it will download. Each of these players strives to optimize their individual quality, which leads to bandwidth competition, causing quality oscillations and buffer starvations. This article proposes a solution to alleviate these problems by deploying in-network quality optimization agents, which monitor the available throughput using sampling-based measurement techniques and optimize the quality of each client, based on a HAS Quality of Experience (QoE) metric. This in-network optimization is achieved by solving a linear optimization problem both using centralized as well as distributed algorithms. The proposed hybrid QoE-driven approach allows the client to take into account the in-network decisions during the rate adaptation process, while still keeping the ability to react to sudden bandwidth fluctuations in the local network. The proposed approach allows improving existing autonomous quality selection heuristics by at least 30%, while outperforming an in-network approach using purely bitrate-driven optimization by up to 19%.

Avoiding Quality Bottlenecks in P2P Adaptive Streaming

This paper addresses the problem of quality bottleneck in adaptive SVC streaming. Quality bottleneck occurs in adaptive streaming systems when the desired video quality cannot be obtained even if the network capabilities are sufficient. In the context of SVC layered video streaming, we have observed that enhancement layers remain around the video source and fail to reach all the participating peers. To overcome this problem, we propose an adaptive SVC streaming solution that cooperatively integrates strategies of overlay formation, data scheduling and content adaptation. Performance evaluation using simulations shows that the proposed streaming solution reduces the quality bottleneck, increases churn-tolerance and optimizes bandwidth utilization.

Adaptive Video Streaming Using TCP Factors Control with User Parameters

Media streaming over TCP has become increasingly popular because TCP's congestion control provides remarkable stability to the Internet. Streaming over TCP requires adapting to bandwidth availability and other network parameters in order to have a good users satisfaction. Nowadays the streaming video can be projected on different kind of terminal as tablet, smart phones, laptop.etc. Each device has its own characteristics and parameters which must be taken into consideration on the video streaming adaptation process. In this paper, we propose an adaptive video streaming solution to improve the quality of experience (QoE) of the users by adapting TCP parameters to the user parameters on mobile networks. We validate the models using the ns2 simulator.

Client intelligence for adaptive streaming solutions

In state-of-the-art adaptive streaming solutions, to cope with varying network conditions, the client side can switch between several video copies encoded at different bit-rates during streaming. Each video copy is divided into chunks of equal duration. To achieve continuous video playback, each chunk needs to arrive at the client before its playback deadline. The perceptual quality of a chunk increases with the chunk size in bits, whereas bigger chunks require more transmission time and, as a result, have a higher risk of missing transmission deadline. Therefore, there is a trade-off between the overall video quality and continuous playback, which can be optimized by proper selection of the next chunk from the encoded versions. This paper proposes a method to compute a set of optimal client strategies for this purpose.