TcpHas: TCP for HTTP adaptive streaming

Ameur, Chiheb Ben; Mory, Emmanuel; Cousin, Bernard; Dedu, Eugen

doi:10.1109/icc.2017.7996614

Cited by 6 publications

(5 citation statements)

References 20 publications

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“…Full-view resolutions of 12K and 24K are anticipated to reach an ultimate level of VR QoE in the future, which in combination with frame rates of up to 200 frames per second requires a typical throughput of up to 4.4 Gbps [5]. Given the high throughput and ultra-low latency requirements of this type of VR applications in the context of VR video streaming over the public Internet, new network architectures are needed to assure QoE as the transmission control protocol no longer meets these stringent demands [6]. This applies in particular to network architectures that contain both fixed and mobile networks.…”

Section: Related Work 1mobile Edge Computing For Vr Videosmentioning

confidence: 99%

Performance Analysis of an Adaptive Rate Scheme for QoE-Assured Mobile VR Video Streaming

Chu

Zepernick

2022

Computers

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The emerging 5G mobile networks are essential enablers for mobile virtual reality (VR) video streaming applications assuring high quality of experience (QoE) at the end-user. In addition, mobile edge computing brings computational resources closer to the user equipment (UE), which allows offloading computationally intensive processing. In this paper, we consider a network architecture for mobile VR video streaming applications consisting of a server that holds the VR video content, a mobile edge virtualization with prefetching (MVP) unit that handles the VR video packets, and a head-mounted display along with a buffer, which together serve as the UE. Several modulation and coding schemes with different rates are provided by the MVP unit to adaptively cope with the varying wireless link conditions to the UE and the state of the UE buffer. The UE buffer caches VR video packets as needed to compensate for the adaptive rates. A performance analysis is conducted in terms of blocking probability, throughput, queueing delay, and average packet error rate. To capture the effect of fading severity, the analytical expressions for these performance metrics are derived for Nakagami-m fading on the wireless link from the MVP unit to the UE. Numerical results show that the proposed system meets the network requirements needed to assure the QoE levels of different mobile VR video streaming applications.

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Section: Related Work 1mobile Edge Computing For Vr Videosmentioning

confidence: 99%

Performance Analysis of an Adaptive Rate Scheme for QoE-Assured Mobile VR Video Streaming

Chu

Zepernick

2022

Computers

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“…However, as this requires client‐side changes to the TCP stack, deployability is limited. The same authors proposed TcpHas, again focusing on the highly periodical ON‐OFF activity pattern of HAS services . This congestion control variant applies server‐based traffic shaping at the TCP layer based on bandwidth and optimal quality level estimations.…”

Section: Related Workmentioning

confidence: 99%

“…The same authors proposed TcpHas, again focusing on the highly periodical ON-OFF activity pattern of HAS services. 17 This congestion control variant applies server-based traffic shaping at the TCP layer based on bandwidth and optimal quality level estimations. Shuai et al propose a transport protocol for low-latency adaptive streaming, relying on a server-side simulation of the client's buffer using an open-loop synchronization mechanism.…”

Section: Related Workmentioning

confidence: 99%

“…When the current throughput measurement T is above T u or below T l , the deadline margin is expected to grow above m u or shrink to less than m l before the end of the stream is reached. To avoid this, the difference m between the current deadline margin and the approaching bound is calculated, as well as the remaining part of the range of values that allows us to change the aggressiveness in the required direction (lines [11][12][13][14][15][16][17][18][19][20]. Based on these values, the critical time period C, by which leaving the margin bounds is expected, can be calculated (line 21).…”

Section: Margin-based Algorithmmentioning

confidence: 99%

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Deadline‐aware TCP congestion control for HTTP adaptive streaming services

et al. 2018

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Summary Over the last decades, video streaming services have gained a lot of popularity, representing over 70% of the total consumer Internet traffic worldwide. While originally dedicated streaming protocols have been used to deliver these services, for several years, the video streaming industry is steadily shifting to HTTP adaptive streaming (HAS)–based delivery over TCP. With each video segment, implicit delivery deadlines are associated in order to avoid playout interruptions. However, TCP is known to be far from minimizing the number of deadline‐missing streams. In this paper, 2 deadline‐aware congestion control mechanisms are proposed, based on a parametrization of the traditional TCP New Reno congestion control strategy. By introducing deadline awareness at the transport layer, the modulation of the congestion window size is dynamically adapted to steer the aggressiveness of the considered stream to optimize the delivery of video streaming services. The proposed approaches are thoroughly evaluated in an HAS video‐on‐demand scenario over tree‐based delivery networks. It was shown that in a realistic scenario, the proposed approaches can outperform traditional congestion control strategies by up to 12% in terms of estimated mean opinion score, mainly by reducing the average video freezing time by more than 85%.

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“…[10] estimated the congestion degree by using the statistics of RTT, thus avoiding unnecessary shrinkage of the lost window, while the fairness cannot be guaranteed among clients. In [11], the authors optimised the bandwidth estimate and slow start threshold to improve the quality of experience (QoE) of clients and the quality of service (QoS) of the network, but the sending window cannot respond to the wireless channel variation in time when the wireless channel varied frequently. Han et al .…”

Section: Introductionmentioning

confidence: 99%

Playback experience driven cross layer optimisation of APP, transport and MAC layer for video clients over long‐term evolution system

Huang

2020

IET Communications

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In traditional communication system, information of APP (Application) layer, transport layer and MAC (Media Access Control) layer has not been fully interacted, which inevitably leads to inconsistencies among TCP congestion state, clients' requirements, and resource allocation. To solve the problem, we propose a joint optimization framework, which consists of APP layer, transport layer and MAC layer, to improve the video clients' playback experience and system throughput. First, a client requirement aware autonomous packet drop strategy, based on packet importance, channel condition and playback status, is developed to decrease the network load and the probability of rebuffering events. Further, TCP (Transmission Control Protocol) state aware downlink and uplink resource allocation schemes are proposed to achieve smooth video transmission and steady ACK (Acknowledgement) feedback respectively. For downlink scheme, maximum transmission capacity requirement for each client is calculated based on feedback ACK information from transport layer to avoid allocating excessive resource to the client, whose ACK feedback is blocked due to bad uplink channel condition. For uplink scheme, information of RTO (Retransmission Timeout) and TCP congestion window are utilized to indicate ACK scheduling priority. The simulation results show that our algorithm can significantly improve the system throughput and the clients' playback continuity with acceptable video quality.

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TcpHas: TCP for HTTP adaptive streaming

Cited by 6 publications

References 20 publications

Performance Analysis of an Adaptive Rate Scheme for QoE-Assured Mobile VR Video Streaming

Performance Analysis of an Adaptive Rate Scheme for QoE-Assured Mobile VR Video Streaming

Deadline‐aware TCP congestion control for HTTP adaptive streaming services

Playback experience driven cross layer optimisation of APP, transport and MAC layer for video clients over long‐term evolution system

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