Multi-codec ultra high definition 8K MPEG-DASH dataset

Taraghi, Babak; Amirpour, Hadi; Timmerer, Christian

doi:10.1145/3524273.3532889

Cited by 19 publications

(5 citation statements)

References 17 publications

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“…We compared the performance of different ABR algorithms and two widely used media players for the HLS and MPEG-DASH. Inspired by the work in [21], we have selected six representations, shown in Table 1. The selection covers a range of very low to high-definition quality streams the server provides for our evaluation.…”

Section: Methodsmentioning

confidence: 99%

LLL-CAdViSE: Live Low-Latency Cloud-Based Adaptive Video Streaming Evaluation Framework

2023

Self Cite

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Live media streaming is a challenging task by itself, and when it comes to use cases that define low-latency as a must, the complexity will rise multiple times. In a typical media streaming session, the main goal can be declared as providing the highest possible Quality of Experience (QoE), which has proved to be measurable using quality models and various metrics. In a low-latency media streaming session, the requirements are to provide the lowest possible delay between the moment a frame of video is captured and the moment that the captured frame is rendered on the client screen, also known as end-to-end (E2E) latency and maintain the QoE. This paper proposes a sophisticated cloud-based and open-source testbed that facilitates evaluating a low-latency live streaming session as the primary contribution. Live Low-Latency Cloud-based Adaptive Video Streaming Evaluation (LLL-CAdViSE) framework is enabled to asses the live streaming systems running on two major HTTP Adaptive Streaming (HAS) formats, Dynamic Adaptive Streaming over HTTP (MPEG-DASH) and HTTP Live Streaming (HLS). We use Chunked Transfer Encoding (CTE) to deliver Common Media Application Format (CMAF) chunks to the media players. Our testbed generates the test content (audiovisual streams). Therefore, no test sequence is required, and the encoding parameters (e.g., encoder, bitrate, resolution, latency) are defined separately for each experiment. We have integrated the ITU-T P.1203 quality model inside our testbed. To demonstrate the flexibility and power of LLL-CAdViSE, we have presented a secondary contribution in this paper; we have conducted a set of experiments with different network traces, media players, ABR algorithms, and with various requirements (e.g., E2E latency (typical/reduced/low/ultra-low), diverse bitrate ladders, and catch-up logic) and presented the essential findings and the experimental results.

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Section: Methodsmentioning

confidence: 99%

LLL-CAdViSE: Live Low-Latency Cloud-Based Adaptive Video Streaming Evaluation Framework

2023

Self Cite

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“…Laboratory that publicly provides a multi-codec DASH dataset [30]. We choose the "Seconds that Count" video sequence as it is the longest available, with a duration of 322 seconds.…”

Section: Methodsmentioning

confidence: 99%

Multi-Layer Monitoring at the Edge for Vehicular Video Streaming: Field Trials

Yeregui

Uriol

Viola

et al. 2023

2023 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

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In an increasingly connected world, wireless networks' monitoring and characterization are of vital importance. Service and application providers need to have a detailed understanding of network performance to offer new solutions tailored to the needs of today's society. In the context of mobility, in-vehicle infotainment services are expected to stand out among other popular connected vehicle services, so it is essential that communication networks are able to satisfy the Quality of Service (QoS) and Quality of Experience (QoE) requirements needed for these type of services. This paper investigates a multi-layer network performance monitoring architecture at the edge providing QoS, QoE, and localization information for vehicular video streaming applications in realtime over 5G networks. In order to conduct field trials and show test results, Mobile Network Operators (MNOs)' 5G Standalone (SA) network and Multi-access Edge Computing (MEC) infrastructure are used to provide connectivity and edge computing resources to a vehicle equipped with a 5G modem.

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“…In this work, we consider the DASH-8K 43 dataset and the GamingVideoSET 44 dataset which are discussed next.…”

Section: Datasetsmentioning

confidence: 99%

“…For exact codec configuration and encoding resolution-bitrate values, please refer to the original publication in Ref. 43. Due to resource limitations, we only consider source sequences of UHD-1 resolution and a duration of two seconds (48 frames).…”

mentioning

confidence: 99%

On the performance of video super-resolution algorithms for HTTP-based adaptive streaming applications

Barman,

Reznik,

Martini

2023

Applications of Digital Image Processing XLVI

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Recent years have seen tremendous growth and advancement in the field of super-resolution algorithms for both images and videos. Such algorithms are mainly based on deep learning technologies and are primarily used for upsampling lower resolution images and videos, often outperforming existing traditional upsampling algorithms. Using such advanced upscaling algorithms on the client side can result in significant bandwidth and storage savings as the client can simply request lower-resolution images/videos and then upscale them to the required (higher) display resolution. However, the performance analysis of such proposed algorithms has been limited to a few datasets which are not representative of modern-era adaptive bitrate video streaming applications. Also, many times they only consider scaling artefacts, and hence their performance when considering typical compression artefacts is not known. In this paper, we evaluate the performance of such AI-based upscaling algorithms on different datasets considering a typical adaptive streaming system. Different content types, video compression standards and renditions are considered. Our results indicate that the performance of video upsampling algorithms measured objectively in terms of PSNR and SSIM is insignificant compared to traditional upsampling algorithms. However, more detailed analysis in terms of other advanced quality metrics as well as subjective tests are required for a comprehensive evaluation.

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Multi-codec ultra high definition 8K MPEG-DASH dataset

Cited by 19 publications

References 17 publications

LLL-CAdViSE: Live Low-Latency Cloud-Based Adaptive Video Streaming Evaluation Framework

LLL-CAdViSE: Live Low-Latency Cloud-Based Adaptive Video Streaming Evaluation Framework

Multi-Layer Monitoring at the Edge for Vehicular Video Streaming: Field Trials

On the performance of video super-resolution algorithms for HTTP-based adaptive streaming applications

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