Live streaming of 4K ultra-high definition video over the internet

Petrangeli, Stefano; Hooft, Jeroen van der; Wauters, Tim; Huysegems, Rafael; Alface, Patrice Rondão; Bostoen, Tom; Turck, Filip De

doi:10.1145/2910017.2910627

Cited by 7 publications

(3 citation statements)

References 9 publications

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“…However, it is shown that when using 1s segments, server push itself does not introduce any benefit in live stream latency. In [24], the authors further reduced the segment length to as low as 0.1s. They designed an SDN controller to prioritize video traffic and hence reduce buffering.…”

Section: Qoe Improvement Techniques Of Http Live Streamingmentioning

confidence: 99%

QoE-Assured 4K HTTP Live Streaming via Transient Segment Holding at Mobile Edge

Wang

Chai

et al. 2018

IEEE J. Select. Areas Commun.

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Abstract-HTTP-based live streaming has become increasingly popular in recent years, and more users have started generating 4K live streams from their devices (e.g., mobile phones) through social-media service providers like Facebook or YouTube. If the audience is located far from a live stream source across the global Internet, TCP throughput becomes substantially suboptimal due to slow-start and congestion control mechanisms. This is especially the case when the end-to-end content delivery path involves radio access network (RAN) at the last mile. As a result, the data rate perceived by a mobile receiver may not meet the high requirement of 4K video streams, which causes deteriorated Quality-of-Experience (QoE). In this paper, we propose a scheme named Edge-based Transient Holding of Live sEgment (ETHLE), which addresses the issue above by performing context-aware transient holding of video segments at the mobile edge with virtualized content caching capability. Through holding the minimum number of live video segments at the mobile edge cache in a context-aware manner, the ETHLE scheme is able to achieve seamless 4K live streaming experiences across the global Internet by eliminating buffering and substantially reducing initial startup delay and live stream latency. It has been deployed as a virtual network function at an LTE-A network, and its performance has been evaluated using real live stream sources that are distributed around the world. The significance of this paper is that by leveraging virtualized caching resources at the mobile edge, we address the conventional transport-layer bottleneck and enable QoE-assured Internet-wide live streaming services with high data rate requirements.Index Terms-HTTP live streaming, mobile edge computing, network function virtualization, quality of experience, video caching

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Section: Qoe Improvement Techniques Of Http Live Streamingmentioning

confidence: 99%

QoE-Assured 4K HTTP Live Streaming via Transient Segment Holding at Mobile Edge

Wang

Chai

et al. 2018

IEEE J. Select. Areas Commun.

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“…Mirshokraie et al optimized video encoding using layered encoding for peer-to-peer streaming [40]. Other transmission optimization techniques to improve live streaming include the use of HTTP/2 to reduce the number of requests [31,45], a hybrid of broadband and multicast [36], optimization of TCP congestion control [7], multiserver streaming [12], scheduling users to different CDNs [65], and selective retransmission of frames [50].…”

Section: Related Workmentioning

confidence: 99%

Playing chunk-transferred DASH segments at low latency with QLive

Yadav

Bentaleb

Lim

et al. 2021

Proceedings of the 12th ACM Multimedia Systems Conference

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More users have a growing interest in low latency over-the-top (OTT) applications such as online video gaming, video chat, online casino, sports betting, and live auctions. OTT applications face challenges in delivering low latency live streams using Dynamic Adaptive Streaming over HTTP (DASH) due to large playback buffer and video segment duration. A potential solution to this issue is the use of HTTP chunked transfer encoding (CTE) with the common media application format (CMAF). This combination allows the delivery of each segment in several chunks to the client, starting before the segment is fully available in real-time. However, CTE and CMAF alone are not sufficient as they do not address other limitations and challenges at the client-side, including inaccurate bandwidth measurement, latency control, and bitrate selection.In this paper, we leverage a simple and intuitive method to resolve the fundamental problem of bandwidth estimation for low latency live streaming through the use of a hybrid of an existing chunk parser and proposed filtering of downloaded chunk data. Next, we model the playback buffer as a / /1/ queue to limit the playback delay. The combination of these techniques is collectively called QLive. QLive uses the relationship between the estimated bandwidth, total buffer capacity, instantaneous playback speed, and buffer occupancy to decide the playback speed and the bitrate of the representation to download. We evaluated QLive under a diverse set of scenarios and found that it controls the latency to meet the given latency requirement, with an average latency up to 21 times lower than the compared methods. The average playback speed of QLive ranges between 1.01 -1.26× and it plays back at 1× speed up to 97% longer than the compared algorithms, without sacrificing the quality of the video. Moreover, the proposed bandwidth estimator has a 94% accuracy and is unaffected by a spike in instantaneous playback latency, unlike the compared state-of-the-art counterparts.

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“…In several publications so far, this topic has been discussed, problems identified and several solutions proposed. However, in most cases, these solutions suffer from low quality, large end-to-end latency in live streaming and frequent freezes in the video playout due to sudden drops of the available bandwidth [22]. In [22], it was shown network-based prioritization introduced by an OpenFlow, SDN-enabled controller can reduce video freezes caused by network congestion.…”

Section: Ultra/high Definition Live Video Streaming In Wireless Networkmentioning

confidence: 99%

Big Data in 5G Distributed Applications

Nejković

Visa

Tošić

et al. 2019

Lecture Notes in Computer Science

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Fifth generation mobile networks (5G) will rather supplement than replace current 4G networks by dramatically improving their bandwidth, capacity and reliability. This way, much more demanding use cases that simply are not achievable with today's networks will become reality-from home entertainment, to product manufacturing and healthcare. However, many of them rely on Internet of Things (IoT) devices equipped with low-cost transmitters and sensors that generate enormous amount of data about their environment. Therefore, due to large scale of 5G systems, combined with their inherent complexity and heterogeneity, Big Data and analysis techniques are considered as one of the main enablers of future mobile networks. In this work, we recognize 5G use cases from various application domains and list the basic requirements for their development and realization.

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Live streaming of 4K ultra-high definition video over the internet

Cited by 7 publications

References 9 publications

QoE-Assured 4K HTTP Live Streaming via Transient Segment Holding at Mobile Edge

QoE-Assured 4K HTTP Live Streaming via Transient Segment Holding at Mobile Edge

Playing chunk-transferred DASH segments at low latency with QLive

Big Data in 5G Distributed Applications

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