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SR-abr: Super Resolution Integrated abr algorithm for Cloud-Based Video Streaming

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年第1期9卷 87-98页

作者： Wu, Haiqiao Wu, Dapeng Oliver Gong, Peng Beijing Inst Technol Sch Mechatron Engn Beijing 100811 Peoples R China Purple Mt Labs Nanjing 210023 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

Super-resolution is a promising solution to improve the quality of experience (QoE) for cloud-based video streaming when the network resources between clients and the cloud vendors become scarce. Specifically, the received video can be enhanced with a trained super-resolution model running on the client-side. However, all the existing solutions ignore the content-induced performance variability of Super-Resolution Deep Neural Network (SR-DNN) models, which means the same super-resolution models have different enhancement effects on the different parts of videos because of video content variation. That leads to unreasonable bitrate selection, resulting in low video QoE, e.g., low bitrate, rebuffering, or video quality jitters. Thus, in this paper, we propose SR-abr, a super-resolution integrated adaptive bitrate (abr) algorithm, which considers the content-induced performance variability of SR-DNNs into the bitrate decision process. Due to complex network conditions and video content, SR-abr adopts deep reinforcement learning (DRL) to select future bitrate for adapting to a wide range of environments. Moreover, to utilize the content-induced performance variability of SR-DNNs efficiently, we first define the performance variability of SR-DNNs over different video content, and then use a 2D convolution kernel to distill the features of the performance variability of the SR-DNNs to a short future video segment (several chunks) as part of the inputs. We compare SR-abr with the related state-of-the-art works using trace-driven simulation under various real-world traces. The experiments show that SR-abr outperforms the best state-of-the-art work NAS with the gain in average QoE of 4.3%-46.2% and 18.9%-42.1% under FCC and 3G/HSDPA network traces, respectively.

关键词： Streaming media Bit rate Superresolution Quality of experience Bandwidth Video recording Training Video streaming abr algorithm super-resolution deep reinforcement learning

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Enhancing QoE of Adaptive Video Streaming by Generating Fine-Grained Throughput

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2025年第4期35卷 3853-3866页

作者： An, Congkai Zhang, Huanhuan Kang, Jingyang Liu, Zhuo Zhou, Anfu Liu, Liang Ma, Huadong Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

On-demand video streaming continues to dominate the Internet, posing a formidable challenge in designing efficient adaptive bitrate (abr) algorithms to enhance user quality-of-experience (QoE), particularly amplified by increasing video resolutions (e.g., from 1080P to 2K, 4K, and even 8K) and dynamic Internet conditions. Through a comprehensive study, we identify a common limitation in both existing throughput-based and hybrid-based abr algorithms: they rely on coarse-grained network bandwidth estimation, missing detailed and accurate (i.e., millisecond-level) network variations. This often leads to misguided resolution (corresponding to bitrate level) decisions, resulting in unsatisfactory QoE. In this work, we propose Superabr, a fine-grained throughput-driven abr solution aimed at achieving the optimal bitrate adaptation. To accomplish this, Superabr first incorporates a two-stage learning module, generating fine-grained future throughput to provide a near-Oracle network view. Superabr then uses this fine-grained throughput to accurately calculate the download duration for a video chunk, transforming it into the optimal resolution decision via a custom-designed QoE benefit model. We have implemented Superabr as a lightweight plug-in interface on a standard DASH framework and evaluate it over extensive real-world network traces. Extensive experiments demonstrate that Superabr can generate accurate future throughput, resulting in a remarkable 1.21 similar to 1.46x QoE improvement over classic abr solutions.

关键词： abr algorithm fine-grained throughput QoE optimization

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

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IEEE ACCESS 2022年 10卷 109565-109579页

作者： Nguyen, Minh Lorenzi, Daniele Tashtarian, Farzad Hellwagner, Hermann Timmerer, Christian Univ Klagenfurt Christian Doppler Lab ATHENA A-9020 Klagenfurt Am Worthersee Austria

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 "How can new features of the recent HTTP versions be used to enhance the performance of HAS?" 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) stream multiplexing, (ii) stream priority, and (iii) request cancellation 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) average bitrate and the (ii) video instability 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://***/cd-athena/DoFP-Pl

关键词： Streaming media Quality of experience Throughput Bit rate Servers Multiplexing Internet HTTP 3 abr algorithm QoE HAS DASH

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Improving quality of experience in adaptive low latency live streaming

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第6期83卷 15957-15983页

作者： Lyko, Tomasz Broadbent, Matthew Race, Nicholas Nilsson, Mike Farrow, Paul Appleby, Steve Univ Lancaster Sch Comp & Commun Lancaster England Edinburgh Napier Univ Sch Comp Edinburgh Scotland British Telecommun PLC Appl Res Martlesham England

HTTP Adaptive Streaming (HAS), the most prominent technology for streaming video over the Internet, suffers from high end-to-end latency when compared to conventional broadcast methods. This latency is caused by the content being delivered as segments rather than as a continuous stream, requiring the client to buffer significant amounts of data to provide resilience to variations in network throughput and enable continuous playout of content without stalling. The client uses an Adaptive Bitrate (abr) algorithm to select the quality at which to request each segment to trade-off video quality with the avoidance of stalling to improve the Quality of Experience (QoE). The speed at which the abr algorithm responds to changes in network conditions influences the amount of data that needs to be buffered, and hence to achieve low latency the abr needs to respond quickly. Llama (Lyko et al. 28) is a new low latency abr algorithm that we have previously proposed and assessed against four on-demand abr algorithms. In this article, we report an evaluation of Llama that demonstrates its suitability for low latency streaming and compares its performance against three state-of-the-art low latency abr algorithms across multiple QoE metrics and in various network scenarios. Additionally, we report an extensive subjective test to assess the impact of variations in video quality on QoE, where the variations are derived from abr behaviour observed in the evaluation, using short segments and scenarios. We publish our subjective testing results in full and make our throughput traces available to the research community.

关键词： CMAF DASH abr algorithm Live streaming Low latency Quality of Experience Video quality assessment Database

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MEDUSA: A Dynamic Codec Switching Approach in HTTP Adaptive Streaming

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ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 2024年第10期20卷 1-23页

作者： Lorenzi, Daniele Tashtarian, Farzad Hellwagner, Hermann Timmerer, Christian Alpen Adria Univ Klagenfurt Inst Informat Technol Christian Doppler Lab ATHENA Klagenfurt Austria

HTTP Adaptive Streaming (HAS) solutions utilize various Adaptive BitRate (abr) algorithms to dynamically select appropriate video representations, aiming at adapting 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 article, 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 object

关键词： HTTP adaptive streaming multi-codec QoE abr algorithm HAS

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QoE Estimation of DASH-Based Mobile Video Application Using Deep Reinforcement Learning 20th

QoE Estimation of DASH-Based Mobile Video Application Using ...

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20th International Conference on algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Hou, Biao Zhang, Junxing Inner Mongolia Univ Hohhot Peoples R China

ISBN: (纸本)9783030602390;9783030602383

An increasing number of video content providers have adopted adaptive bitrate (abr) streaming via the HTTP protocol. The client players usually run an abr algorithm to determine the optimal quality of video playback in the next few seconds. Faced with unpredictable bandwidth variability, the latest abr algorithm attempts to achieve the best balance between competing goals of high bitrate, less rebuffering and high smoothness. However, there is no guarantee that optimal resource utilization ensures a high quality of experience (QoE). QoE is also affected by users' preferences for video content. Even for the same movie clip, different users have varied preferences for characters, scenes, plots and other content. In this paper, we propose a Deep-Q Learning Network (DQN) based abr algorithm to optimize the use of network and client resources in video playback and also improve QoE of users.

关键词： MPEG-DASH protocol abr algorithm QoE model DQN algorithm

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A Content-Aware Deep Q-Learning Approach for Adaptive Video Streaming 6

A Content-Aware Deep Q-Learning Approach for Adaptive Video ...

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6th International Conference on Communications Signal Processing and their Applications

作者： Amer, Hala Hassan, Mohamed S. Ismail, Mahmoud H. Amer Univ Sharjah Dept Elect Engn Sharjah U Arab Emirates

ISBN: (纸本)9798350384826;9798350384819

Adaptive streaming over HTTP aims to maximize user Quality-of-Experience through video quality adaptation. Conventional adaptation schemes measure the video quality for variable bitrate (VBR) video in terms of the average bitrate. However, video bitrate is not an accurate measure of perceptual quality. Alternative quality measures, such as the Video Multimethod Assessment Fusion (VMAF), can be used to better represent the quality perceived by the viewer. Studying the VMAF of video chunks across the same bitrate level shows that user QoE depends not only on the overall video quality, but also on the content complexity. This work proposes a deep Qlearning (DQL) adaptation algorithm that accounts for content complexity by prioritizing complex video chunks during bitrate selection. Simulation results show that the average video quality is improved by using complexity-aware streaming over the baseline algorithms.

关键词： HTTP adaptive streaming deep Q-learning complexity-aware adaptation abr algorithm

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Quality of Experience in Video Streaming: Status Quo, Pitfalls, and Guidelines 16

Quality of Experience in Video Streaming: Status Quo, Pitfal...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Peroni, Leonardo Gorinsky, Sergey Imdea Networks Institute UC3M Madrid Spain Imdea Networks Institute Madrid Spain

ISBN: (纸本)9798350383119

Quality of experience (QoE) becomes both the holy grail and a free-for-all in adaptive bitrate (abr) video streaming. On the one hand, the design, operation, and evaluation of abr algorithms increasingly rely on QoE. On the other hand, QoE frequently receives only cursory attention in this supporting role, with many of its important aspects treated with insufficient care. As a complex subjective notion, QoE is directly measurable through subjective tests, which incur evident overhead. While an objective QoE model represents a scalable automated means for QoE assessment, QoE models proliferate without consensus on their goodness due to numerous influence factors, construction methods, and usages. The model proliferation creates a false impression that proposing a new QoE model without a proper validation is acceptable. Because the multifaceted QoE problem involves separable and often separated tasks of test conducting, model building, and model using, this separation of concerns causes additional complications. By leveraging two large real datasets of individual QoE perception, this paper reviews the status quo in QoE, identifies various pitfalls, and offers guidelines for test conducting, model building, and model using, so as to foster high standards in future work on QoE in abr video streaming. © 2024 IEEE.

关键词： abr algorithm evaluation metric experience selection interface design QoE model quality of experience range capping scoring scale subjective test value interpretability Video streaming

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ANFIS-based rate adaptation scheme for adaptive streaming over HTTP

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2018年第1期2018卷 1页

作者： Son, Ye Seul Kim, Hyun Jun Kim, Joon Tae Konkuk Univ Dept Elect Engn Seoul South Korea

Recently, HTTP-based video streaming traffic has continued to increase. Therefore, video service providers have been using HTTP-based adaptive streaming (HAS) technology to reduce the traffic load of the HTTP server. Accordingly, many adaptive bit rate (abr) schemes have been proposed to provide a high quality of experience (QoE) to video service clients. In this paper, we propose a new abr scheme using an adaptive network-based fuzzy inference system (ANFIS), which is one of the neuro-fuzzy structures. The proposed scheme learns optimal fuzzy parameters by using (1) the learning ability of ANFIS and (2) the video streaming data providing high QoE to clients. Then, the bit rate of the next segment is determined according to these trained parameters. In the simulation using NS-3, we show that the proposed scheme selects the appropriate bit rate under various wireless network conditions and provides better QoE to clients than the existing schemes.

关键词： HAS technology MPEG-DASH abr algorithm Adaptive streaming ANFIS Neuro-fuzzy system

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