Encoding Bitrate Optimization Using Playback Statistics for HTTP-based Adaptive Video Streaming

Chen, Chao; Kokaram, Anil; Benting, Steve

doi:10.48550/arxiv.1709.08763

Cited by 6 publications

(8 citation statements)

References 11 publications

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“…Over the years, adaptive video streaming was primarily investigated for the purpose of entertainment services. For instance, the approach in [8] optimizes playback bitrates using pre-generated rate-quality models and other statistics. Other rate control methods, such as look-ahead approaches [9,10] enable variable allocation of a constant total bitrate for multiple videos.…”

Section: Adaptive Multi-camera Video Streamingmentioning

confidence: 99%

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Adaptive Video Configuration and Bitrate Allocation for Teleoperated Vehicles

Schimpe¹,

Hoffmann²,

Diermeyer³

2021

Preprint

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Vehicles with autonomous driving capabilities are present on public streets. However, edge cases remain that still require a human in-vehicle driver. Assuming the vehicle manages to come to a safe state in an automated fashion, teleoperated driving technology enables a human to resolve the situation remotely by a control interface connected via a mobile network. While this is a promising solution, it also introduces technical challenges, one of them being the necessity to transmit video data of multiple cameras from the vehicle to the human operator. In this paper, an adaptive video streaming framework specifically designed for teleoperated driving is proposed and demonstrated. The framework enables automatic reconfiguration of the video streams of the multi-camera system at runtime. Predictions of variable transmission service quality are taken into account. With the objective to maximize visual quality, the framework uses so-called rate-quality models to dynamically allocate bitrates and select resolution scaling factors. Results from deploying the proposed framework on an actual teleoperated driving system are presented.

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Section: Adaptive Multi-camera Video Streamingmentioning

confidence: 99%

“…To perform the previously described resolution scaling factor selection, rate-quality models are generated beforehand. This follows a concept presented in [8], which splits a video in five second, non-overlapping chunks. Each chunk is encoded at different resolutions, and compression rates.…”

Section: Generation Of Rate-quality Modelsmentioning

confidence: 99%

Adaptive Video Configuration and Bitrate Allocation for Teleoperated Vehicles

Schimpe¹,

Hoffmann²,

Diermeyer³

2021

Preprint

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show abstract

“…with an approximation error given by where | • | 2 indicates the Euclidean norm of a vector, and h n denotes the discrete version of a basis h n . Given M empirical GRD functions in the Waterloo GRD database, the optimal orthonormal basis is thus obtained by minimizing the average approximation error, as defined in (12), for any number of bases N :…”

Section: B Optimal Basis For Real-world Grd Functionsmentioning

confidence: 99%

“…Many multimedia applications depend on precise measurements of the RD relationship to characterize source video signal, maximize user Quality-of-Experience (QoE) and make efficient use of bitrate resources. Examples of such applications include codec evaluation [3], [4], rate-distortion optimization [5], video quality assessment (VQA) [6], encoding representation recommendation [7]- [10], and QoE optimization of streaming videos [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Characterizing Generalized Rate-Distortion Performance of Video Coding: An Eigen Analysis Approach

Duanmu

et al. 2020

IEEE Trans. on Image Process.

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Rate-distortion (RD) analysis is at the heart of lossy data compression. Here we extend the idea to generalized RD (GRD) functions of compressed videos that characterize the visual quality of a video and its encoding profile, which includes not only bit rate but also other attributes such as video resolution. We first define the theoretical functional space of the GRD function by analyzing its mathematical properties. We show that the GRD function space is a convex set in a Hilbert space, inspiring a computational model of the GRD function, based on which a general framework is proposed for GRD function reconstruction from known samples. We collect a large-scale database of GRD functions generated from diverse video contents and encoders. Using the database, we demonstrate that real-world GRD functions are clustered in a low-dimensional subspace in the theoretical space of all possible GRD functions. Combining the GRD reconstruction framework and the learned low-dimensional space, we create a low-parameter eigen GRD (eGRD) method to accurately estimate the GRD function of a source video content from only a few queries. Experimental results show that the proposed algorithm significantly outperforms state-of-the-art empirical RD estimation methods in accuracy and efficiency. Finally, we demonstrate the usefulness of the proposed eGRD model in a practical application: video codec comparison.

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“…Many multimedia applications require precise measurements of ratedistortion functions to characterize source signal and maximize user Quality-of-Experience (QoE). Examples of applications that explicitly use rate-distortion measurements are codec evaluation [1], rate-distortion optimization [2], video quality assessment (VQA) [3], encoding representation recommendation [4]- [7], and QoE optimization of streaming videos [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Modeling Generalized Rate-Distortion Functions

Duanmu,

Liu,

Wang

2019

Preprint

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Many multimedia applications require precise understanding of the rate-distortion characteristics measured by the function relating visual quality to media attributes, for which we term it the generalized rate-distortion (GRD) function. In this study, we explore the GRD behavior of compressed digital videos in a three-dimensional space of bitrate, resolution, and viewing device/condition. Our analysis on a large-scale video dataset reveals that empirical parametric models are systematically biased while exhaustive search methods require excessive computation time to depict the GRD surfaces. By exploiting the properties that all GRD functions share, we develop an Robust Axial-Monotonic Clough-Tocher (RAMCT) interpolation method to model the GRD function. This model allows us to accurately reconstruct the complete GRD function of a source video content from a moderate number of measurements. To further reduce the computational cost, we present a novel sampling scheme based on a probabilistic model and an information measure. The proposed sampling method constructs a sequence of quality queries by minimizing the overall informativeness in the remaining samples. Experimental results show that the proposed algorithm significantly outperforms state-of-the-art approaches in accuracy and efficiency. Finally, we demonstrate the usage of the proposed model in three applications: rate-distortion curve prediction, pertitle encoding profile generation, and video encoder comparison.

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Encoding Bitrate Optimization Using Playback Statistics for HTTP-based Adaptive Video Streaming

Cited by 6 publications

References 11 publications

Adaptive Video Configuration and Bitrate Allocation for Teleoperated Vehicles

Adaptive Video Configuration and Bitrate Allocation for Teleoperated Vehicles

Characterizing Generalized Rate-Distortion Performance of Video Coding: An Eigen Analysis Approach

Modeling Generalized Rate-Distortion Functions

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