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A Buffer-Based adaptive bitrate Approach in Wireless Networks With Iterative Correction

IEEE WIRELESS COMMUNICATIONS LETTERS 2022年第8期11卷 1644-1648页

作者： Liu, Jingling Liu, Zhuoran Huang, Jiawei Jiang, Wenchao Wang, Jianxin Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Singapore Univ Technol & Design Pillar Informat Syst Technol & Design Singapore 487372 Singapore

Most providers of video streaming are interested in improving quality of user experience across various wireless network circumstances. Many buffer-based ABR algorithms have been proposed to adjust the bitrate according to the buffer occupancy at the client player. Though keeping the buffer occupancy stable, such ABR algorithms cannot provide satisfied responsiveness to bandwidth dynamics. To solve the problem, we propose an ABR algorithm BBA+ that iteratively corrects the mapping function between bitrate and buffer occupancy according to network throughput. The results show that BBA+ effectively improves the average bitrate and reduces the rebuffering ratio.

关键词： Bit rate Bandwidth Throughput Streaming media Wireless communication Heuristic algorithms Steady-state Video delivery adaptive bitrate algorithms

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Beyond QoE: Diversity Adaptation in Video Streaming at the Edge

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IEEE-ACM TRANSACTIONS ON NETWORKING 2021年第1期29卷 289-302页

作者： Qiao, Chunyu Wang, Jiliang Liu, Yunhao Tsinghua Univ Sch Software Beijing 100084 Peoples R China Tsinghua Univ Coll Global Innovat Exchange GIX Beijing 100084 Peoples R China

adaptive bitrate (ABR) algorithms are critical techniques for high quality-of-experience (QoE) Internet video delivery. Early ABR algorithms conducting the overall QoE function of fixed parameters are limited by the fact that the QoE of end-users are diverse such that the video bitrate is often chosen in a misleading way. State-of-the-art ABR algorithms like MPC and Pensieve utilize offline modeling techniques and result in performance degradation for online QoE diversity adaptation. To address this issue, we propose Elephanta, an online ABR algorithm for edge users, which incorporates user QoE perception interface and adaptation algorithm with flexible parameters. In order to avoid overhead from updating parameters online, we model video streaming as a renewal system and formulate the specific QoE function into flexible formats by setting constraints on corresponding QoE metrics. To validate parameter settings, we emulate Elephanta under 1500 throughput traces, including FCC broadband, 3G HSDPA data set from the Internet, as well as the 4G/LTE data set we collect. Evaluation results show that Elephanta achieves QoE improvement of 7% over MPC and 3% over Pensieve under QoE diversity in part because of its superior adaptability to QoE diversity. We implemented Elephanta in *** at the client side for subjective experiments. We observed the diverse QoE preferences across users and 19/21 users (strongly) agree that Elephanta is responsive to parameter changes while watching videos.

关键词： Quality of experience Streaming media Bit rate Adaptation models Heuristic algorithms Degradation QoE diversity adaptive bitrate algorithms video streaming

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Verifying Learning-Augmented Systems 21

Verifying Learning-Augmented Systems

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ACM SIGCOMM Conference (SIGCOMM)

作者： Eliyahu, Tomer Kazak, Yafim Katz, Guy Schapira, Michael Hebrew Univ Jerusalem Jerusalem Israel

ISBN: (纸本)9781450383837

The application of deep reinforcement learning (DRL) to computer and networked systems has recently gained significant popularity. However, the obscurity of decisions by DRL policies renders it hard to ascertain that learning-augmented systems are safe to deploy, posing a significant obstacle to their real-world adoption. We observe that specific characteristics of recent applications of DRL to systems contexts give rise to an exciting opportunity: applying formal verification to establish that a given system provably satisfies designer/user-specified requirements, or to expose concrete counter-examples. We present whiRL, a platform for verifying DRL policies for systems, which combines recent advances in the verification of deep neural networks with scalable model checking techniques. To exemplify its usefulness, we employ whiRL to verify natural requirements from recently introduced learning-augmented systems for three real-world environments: Internet congestion control, adaptive video streaming, and job scheduling in compute clusters. Our evaluation shows that whiRL is capable of guaranteeing that natural requirements from these systems are satisfied, and of exposing specific scenarios in which other basic requirements are not.

关键词： deep reinforcement learning deep learning neural networks formal verification networked systems congestion control adaptive bitrate algorithms resource scheduling

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PyDash - A Framework Based Educational Tool for adaptive Streaming Video algorithms Study

PyDash - A Framework Based Educational Tool for Adaptive Str...

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IEEE Frontiers in Education Conference (FIE)

作者： Marotta, Marcelo A. Souza, Gustavo C. Holanda, Maristela Caetano, Marcos F. Univ Brasilia UnB Dept Comp Sci Brasilia DF Brazil

ISBN: (纸本)9781665438513

Full Paper in the Innovative Practice track - The pandemics caused by the spreading of the COVID 19 virus cornered the educational system worldwide, changing the classroom into remote class activities. This change in our social behavior has directly impacted the volume and shape of the Internet traffic data. A recent study shows 15% to 30% increases in Internet traffic caused, among other reasons, by educational video streaming traffic during few weeks in the 2020 lockdown period in Europe. To give some perspective, network providers usually work with a 30% data traffic increase per year. In 2021, it is expected that almost 82% of all Internet traffic will be video, according to CISCO annual forecast report. This scenario has a tremendous impact on the Internet bandwidth capacity, demanding optimized video streaming solutions, such as adaptive bitrate algorithms (ABR). On the other hand, considering the educational challenges in computer network courses, the core activities must be executed using specialized infrastructure to develop students' capabilities with networking equipment. As these types of equipment are costly to be obtained and forwarded to in-home students or simply e-students, a remote platform capable of reproducing an environment for networking applications is required. This is the scenario where PyDash was built. PyDash is a framework for the development of adaptive streaming video algorithms. It is a learning tool designed to abstract the networking communication details, allowing e-students to focus exclusively on developing and evaluating ABR protocols. This paper presents our practical experience developing and using PyDash as an educational tool for teaching ABR protocols at Computing Networking courses at the Department of Computer Science at the University of Brasilia, Brazil. Last semester, over 120 students, divided into four different undergraduate courses, had their first contact with PyDash. Even though this was their first contact with

关键词： Educational Tool adaptive Streaming Video adaptive bitrate algorithms Testbed DASH ABR

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RESA: A REAL-TIME EVALUATION SYSTEM FOR ABR

RESA: A REAL-TIME EVALUATION SYSTEM FOR ABR

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Wang, Yanan Wang, Haili Shang, Jiaoyang Tuo, Hu iQIYI Inc Beijing Peoples R China

ISBN: (纸本)9781538695524

adaptive bitrate Algorithm (ABR) is a category of important technique to improve the Quality of Experience (QoE) of video streaming service. Many ABR algorithms have been proposed recently and achieved good results. However, there are still some problems to be solved: (1) These ABR algorithms are based on open source data and evaluated by only one simulated player;(2) The QoE metrics are defined differently and their parameters are set by experience with little explanation;(3) These algorithms consider only the QoE of users but ignore the bandwidth cost of video service providers. In order to solve these problems, we propose a Real-time Evaluation System for ABR (RESA) which do evaluations by real online users. A reasonable QoE metric is also introduced by setting its parameters based on user preferences during video watching. We evaluate the state-of-the-art ABR algorithms on RESA with real video streaming service. Finally, we further introduce bitrate controlling into the adaptive bitrate Algorithm (ABRbc) to solve the bandwidth cost problem for online ABR.

关键词： Multimedia streaming adaptive bitrate algorithms Quality of Experience

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Vantage: Optimizing video upload for time-shifted viewing of social live streams 19

Vantage: Optimizing video upload for time-shifted viewing of...

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Conference of the ACM-Special-Interest-Group-on-Data-Communication (SIGCOMM)

作者： Ray, Devdeep Kosaian, Jack Rashmi, K. V. Seshan, Srinivasan Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781450359566

Social live video streaming (SLVS) applications are becoming increasingly popular with the rise of platforms such as Facebook-Live, YouTube-Live, Twitch and Periscope. A key characteristic that differentiates this new class of applications from traditional live streaming is that these live streams are watched by viewers at different delays;while some viewers watch a live stream in real-time, others view the content in a time-shifted manner at different delays. In the presence of variability in the upload bandwidth, which is typical in mobile environments, existing solutions silo viewers into either receiving low latency video at a lower quality or a higher quality video with a significant delay penalty, without accounting for the presence of diverse time-shifted viewers. In this paper, we present Vantage, a live-streaming upload solution that improves the overall quality of experience for diverse time-shifted viewers by using selective quality-enhancing retransmissions in addition to real-time frames, optimizing the encoding schedules to balance the allocation of the available bandwidth between the two. Our evaluation using real-world mobile network traces shows that Vantage can provide high quality simultaneously for both low-latency and delayed viewing. For delayed viewing, Vantage achieves an average improvement of 19.9% over real-time optimized video streaming techniques across all the network traces and test videos, with observed gains of up to 42.9%. These benefits come at the cost of an average drop in real-time quality of 3.3%, with a maximum drop of 7.1%. This represents a significant performance improvement over current techniques used for SLVS applications, which primarily optimize the video upload for real-time viewing.

关键词： Video delivery adaptive bitrate algorithms live streaming VOD

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Sammy: smoothing video traffic to be a friendly internet neighbor 23

Sammy: smoothing video traffic to be a friendly internet nei...

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ACM SIGCOMM Conference (SIGCOMM)

作者： Spang, Bruce Kunamalla, Shravya Teixeira, Renata Huang, Te-Yuan Armitage, Grenville Johari, Ramesh McKeown, Nick Stanford Univ Stanford CA 94305 USA Netflix Los Gatos CA USA

ISBN: (纸本)9798400702365

On-demand streaming video traffic is managed by an adaptive bitrate (ABR) algorithm whose job is to optimize quality of experience (QoE) for a single video session. ABR algorithms leave the question of sharing network resources up to transport-layer algorithms. We observe that as the internet gets faster relative to video streaming rates, this delegation of responsibility gives video traffic a burstier on-off traffic pattern. In this paper, we show we can substantially smooth video traffic to improve its interactions with the rest of the internet, while maintaining the same or better QoE for streaming video. We smooth video traffic with two design principles: application-informed pacing, which allows ABR algorithms to set an upper limit on packet-by-packet throughput, and by designing ABR algorithms that work with pacing. We propose a joint ABR and rate-control scheme, called Sammy, which selects both video quality and pacing rates. We implement our scheme and evaluate it at a large video streaming service. Our approach smooths video, making it a more friendly neighbor to other internet applications. One surprising result is that being friendlier requires no compromise for the video traffic: in large scale, production experiments, Sammy improves video QoE over an existing, extensively tested and tuned production ABR algorithm.

关键词： Video streaming adaptive bitrate algorithms Congestion control algorithms Network friendliness

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Oboe: Auto-tuning Video ABR algorithms to Network Conditions 18

Oboe: Auto-tuning Video ABR Algorithms to Network Conditions

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Conference of the ACM-Special-Interest-Group-on-Data-Communication (ACM SIGCOMM)

作者： Akhtar, Zahaib Nam, Yun Seong Govindan, Ramesh Rao, Sanjay Chen, Jessica Katz-Bassett, Ethan Ribeiro, Bruno Zhan, Jibin Zhang, Hui Univ Southern Calif Los Angeles CA 90089 USA Purdue Univ W Lafayette IN 47907 USA Univ Windsor Windsor ON Canada Columbia Univ New York NY 10027 USA Conviva Foster City CA USA

ISBN: (纸本)9781450355674

Most content providers are interested in providing good video delivery QoE for all users, not just on average. State-of-the-art ABR algorithms like BOLA and MPC rely on parameters that are sensitive to network conditions, so may perform poorly for some users and/or videos. In this paper, we propose a technique called Oboe to auto-tune these parameters to different network conditions. Oboe pre-computes, for a given ABR algorithm, the best possible parameters for different network conditions, then dynamically adapts the parameters at run-time for the current network conditions. Using testbed experiments, we show that Oboe significantly improves BOLA, MPC, and a commercially deployed ABR. Oboe also betters a recently proposed reinforcement learning based ABR, Pensieve, by 24% on average on a composite QoE metric, in part because it is able to better specialize ABR behavior across different network states.

关键词： Video delivery adaptive bitrate algorithms

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Beyond QoE: Diversity Adaption in Video Streaming at the Edge 39

Beyond QoE: Diversity Adaption in Video Streaming at the Edg...

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39th IEEE International Conference on Distributed Computing Systems (ICDCS)

作者： Qiao, Chunyu Wang, Jiliang Liu, Yunhao Tsinghua Univ Sch Software Beijing Peoples R China Michigan State Univ Dept Comp Sci & Engn E Lansing MI 48824 USA

ISBN: (纸本)9781728125190

adaptive bitrate (ABR) algorithms have been critical techniques for high quality-of-experience (QoE) Internet video delivery. Prior work designs ABR algorithms by conducting the overall QoE function of fixed parameters. However, the QoE of end users are diverse and video bitrate may be chosen in a misleading way when leaving out the diversity. State-of-the-art ABR algorithms like MPC, Pensieve utilize off-line modeling techniques and result in performance degradation for online QoE diversity adaption. To address this issue, we propose Elephanta, an online flexible ABR algorithm for edge users which incorporates (1) user QoE perception interface and (2) adaption algorithm with flexible parameters. To avoid overheads for updating parameters online, we model video streaming as a renewal system and formulate specific QoE function into flexible formats by setting constraints on corresponding QoE metrics. To validate parameter setting, we emulate Elephanta under 5 thousand throughput traces including FCC broadband, 3G HSDPA data set from the Internet and 4G/LTE data set collected by ourselves. Accordingly, we implement Elephanta in *** at client side for user test. Evaluation results show that Elephanta achieves QoE improvement by 21.1% over MPC, in part for its superior adaptability to QoE diversity.

关键词： QoE diversity adaptive bitrate algorithms video streaming

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Live Video Streaming Optimization Based on Deep Reinforcement Learning 2020

Live Video Streaming Optimization Based on Deep Reinforcemen...

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12th International Conference on Machine Learning and Computing (ICMLC)

作者： Zhang, Xueshuai Hu, Yuxiang Li, Ziyong Natl Digital Switching Syst Engn & Res Ctr Zhengzhou 450002 Peoples R China

ISBN: (纸本)9781450376426

Video players employ adaptive bitrate algorithms in video-on-demand (VoD) scenarios to improve user-perceived quality of experience (QoE), whereas performance will obviously decline in live video streaming scenarios. To this end, we propose a novel deep reinforcement learning (DRL) based live video streaming optimization approach. Firstly, we point out the optimization objectives by comparing the difference between the VoD scenario and the live video streaming scenario. Then, according to the optimization conditions, we establish QoE optimization model in combination with a state-of-the-art DRL algorithm. We compare our algorithm with state-of-the-art ABR algorithms in a simulator with real-world video and network trace. Simulation results show that the proposed algorithm improves user experience quality by 5.6% on average, compared with existing algorithms.

关键词： live video streaming deep reinforcement learning adaptive bitrate algorithms QoE

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