Multi-codec DASH dataset

Zabrovskiy, Anatoliy; Feldmann, Christian; Timmerer, Christian

doi:10.1145/3204949.3208140

Cited by 44 publications

(28 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Finally, Zabrovskiy et al [187] provide a multi-codec DASH dataset comprising multiple state-of-the-art as well as emerging video codecs, i.e., AVC, HEVC, VP9, and AV1 to enable interoperability testing and allow for experimenting with adaptation strategies of DASH clients supporting multiple video codecs. A similar dataset is provided by Quinlan and Sreenan [188] focusing on AVC and HEVC for UHD (4K) resolutions.…”

Section: Has Datasetsmentioning

confidence: 99%

A Survey on Bitrate Adaptation Schemes for Streaming Media Over HTTP

Bentaleb

Taani

Begen

et al. 2019

IEEE Commun. Surv. Tutorials

Self Cite

362

157

View full text Add to dashboard Cite

In this survey, we present state-of-the-art bitrate adaptation algorithms for HTTP adaptive streaming (HAS). As a key distinction from other streaming approaches, the bitrate adaptation algorithms in HAS are chiefly executed at each client, i.e., in a distributed manner. The objective of these algorithms is to ensure a high quality of experience (QoE) for viewers in the presence of bandwidth fluctuations due to factors like signal strength, network congestion, network reconvergence events, etc. While such fluctuations are common in public Internet, they can also occur in home networksor even managed networks where there is often admission control and QoS tools. Bitrate adaptation algorithms may take factors like bandwidth estimations, playback buffer fullness, device features, viewer preferences, and content features into account, albeit with different weights. Since the viewer's QoE needs to be determined in real-time during playback, objective metrics are generally used including number of buffer stalls, duration of startup delay, frequency and amount of quality oscillations, and video instability. By design, the standards for HAS do not mandate any particular adaptation algorithm, leaving it to system builders to innovate and implement their own method. This survey provides an overview of the different methods proposed over the last several years.

show abstract

Section: Has Datasetsmentioning

confidence: 99%

A Survey on Bitrate Adaptation Schemes for Streaming Media Over HTTP

Bentaleb

Taani

Begen

et al. 2019

IEEE Commun. Surv. Tutorials

Self Cite

362

157

View full text Add to dashboard Cite

show abstract

“…This step selects videos of different type for proper training and validation. Some sequences have minor movements such as moving head on a static black background, while others have significant movements such as changing street view or riding jockeys [2]. After the video sequence selection, the next step splits the video files in different segments, for example 2 s, 4 s or 10 s long, as commonly used in industry.…”

Section: Video Sequence Selection and Segmentationmentioning

confidence: 99%

“…We explain these phases and their interaction in the remaining of this section. In this phase, we selected ten video sequences from our previous work [2] comprising different types of video content in YUV format with the characteristics shown in Table 3. The video sequences BBB, Sintel and TOS contain animation content (fully or mixed), ReadySetGo and Jocky are sports-related, HoneyBee and WindAndNature are of nature category, while YachtRide, DrivingPOV and Beauty classified as general do not match any category.…”

Section: • We Identified Different Features Values For 19 Bitratesmentioning

confidence: 99%

“…According to Cisco's annual Internet report (2019-2023) [1], video applications and services are in high demand with ever increasing requirements in video quality and network bandwidth. Therefore, processing and transmitting video content over the Internet as quickly as possible and in all qualities is crucial [2]. Nowadays, adaptive streaming formats such as MPEG-DASH [3] and HTTP live streaming (HLS) [4] significantly improve the video quality of Internet streaming services [5].…”

Section: Introductionmentioning

confidence: 99%

“…Adaptive 360°v ideo streaming applications [10,11] widely adjust the video quality for the field of view and the rest of the image. Unfortunately, creating segments of a single video for adaptive streaming can take seconds or even hours [12], depending on many technical aspects and features, such as video codec, video file characteristics, transcoding features, and processing capabilities [2].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

FastTTPS: fast approach for video transcoding time prediction and scheduling for HTTP adaptive streaming videos

Agrawal

Zabrovskiy

Ilangovan³

et al. 2020

Cluster Comput

Self Cite

View full text Add to dashboard Cite

HTTP adaptive streaming of video content becomes an integrated part of the Internet and dominates other streaming protocols and solutions. The duration of creating video content for adaptive streaming ranges from seconds or up to several hours or days, due to the plethora of video transcoding parameters and video source types. Although, the computing resources of different transcoding platforms and services constantly increase, accurate and fast transcoding time prediction and scheduling is still crucial. We propose in this paper a novel method called fast video transcoding time prediction and scheduling (FastTTPS) of x264 encoded videos based on three phases: (i) transcoding data engineering, (ii) transcoding time prediction, and (iii) transcoding scheduling. The first phase is responsible for video sequence selection, segmentation and feature data collection required for predicting the transcoding time. The second phase develops an artificial neural network (ANN) model for segment transcoding time prediction based on transcoding parameters and derived video complexity features. The third phase compares a number of parallel schedulers to map the predicted transcoding segments on the underlying high-performance computing resources. Experimental results show that our predictive ANN model minimizes the transcoding mean absolute error (MAE) and mean square error (MSE) by up to 1.7 and 26.8, respectively. In terms of scheduling, our method reduces the transcoding time by up to 38% using a Max–Min algorithm compared to the actual transcoding time without prediction information.

show abstract

Performance analysis of H2BR: HTTP/2-based segment upgrading to improve the QoE in HAS

Nguyen

Amirpour

Tashtarian

et al. 2023

Multimed Tools Appl

View full text Add to dashboard Cite

HTTP Adaptive Streaming (HAS) plays a key role in over-the-top video streaming with the ability to reduce the video stall duration by adapting the quality of transmitted video segments to the network conditions. However, HAS still suffers from two problems. First, it incurs variations in video quality because of throughput fluctuation. Adaptive bitrate (ABR) algorithms at the HAS client usually select a low-quality segment when the throughput drops to avoid stall events, which impairs the Quality of Experience (QoE) of the end-users. Second, many ABR algorithms choose the lowest-quality segments at the beginning of a video streaming session to ramp up the playout buffer early on. Although this strategy decreases the startup time, clients can be annoyed as they have to watch a low-quality video initially. To address these issues, we introduced the H2BR technique (H TTP/2-B ased R etransmission) (Nguyen et al. 33) that utilizes certain features of HTTP/2 (including server push, multiplexing, stream priority, and stream termination) for late transmissions of higher-quality versions of video segments already in the client buffer, in order to improve video quality. Although H2BR was shown to enhance the QoE, limited streaming scenarios were considered resulting in a lack of general conclusions on H2BR’s performance. Thus, this article provides a profound evaluation to answer three open questions: (i) how H2BR’s performance is impacted by parameters at the server side (i.e., various encoding specifications), at the network side (i.e., packet loss rate), and at the client side (i.e., buffer size) on the performance of H2BR; (ii) how H2BR outperforms other state-of-the-art approaches in different configurations of the parameters above; (iii) how to effectively utilize H2BR on top of ABR algorithms in various streaming scenarios. The experimental results show that H2BR’s performance increases with the buffer size and decreases with increasing packet loss rates and/or video segment duration. The number of quality levels can negatively or positively impact on H2BR’s performance, depending on the ABR algorithm deployed. In general, H2BR is able to enhance the video quality by up to 17% and 14% in scalable video streaming and in non-scalable video streaming, respectively. Compared with an existing retransmission technique (i.e., SQUAD Wang et al., ACM Trans Multimed Comput Commun Applic (TOMM) 13(3s): 45, 49), H2BR shows better results with more than 10% in QoE and 9% in the average video quality.

show abstract

Multi-codec DASH dataset

Cited by 44 publications

References 12 publications

A Survey on Bitrate Adaptation Schemes for Streaming Media Over HTTP

A Survey on Bitrate Adaptation Schemes for Streaming Media Over HTTP

FastTTPS: fast approach for video transcoding time prediction and scheduling for HTTP adaptive streaming videos

Performance analysis of H2BR: HTTP/2-based segment upgrading to improve the QoE in HAS

Contact Info

Product

Resources

About