Cache-Enabled Adaptive Video Streaming Over Vehicular Networks: A Dynamic Approach

Guo, Yashuang; Yang, Qinghai; Yu, F. Richard; Leung, Victor C. M.

doi:10.1109/tvt.2018.2817210

Cited by 75 publications

(37 citation statements)

References 32 publications

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“…A niche application of caching is video streaming in vehicular networks. Guo et al [29] proposed a time-scaled caching scheme for Adaptive Bit Rate (ABR) video streaming in vehicular networks, in which caching is performed at base station (BS). This scheme manages the video quality, cache placement, and video transmission.…”

Section: Storage As a Servicementioning

confidence: 99%

Services and simulation frameworks for vehicular cloud computing: a contemporary survey

Ahmed

Malik

Hafeez

et al. 2019

J Wireless Com Network

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Vehicular cloud is getting significant research attention due to the technological advancements in smart vehicles. In near future, vehicles are envisioned to become part of a grid network providing cloud services, such as computing, storage, network, and application as a service. Vehicular cloud computing is an emerging area, designed to support delay-sensitive applications. However, this integration of vehicular network and cloud computing introduces new challenges for the research community. New frameworks have been proposed to assimilate and efficiently manage this merger. In this survey paper, we present the recent advancements in vehicular cloud computing domain. The review is primarily focused on two areas. First, we discuss the frameworks designed to utilize the vehicles' onboard resources to provide cloud services and highlight the design issues and research challenges. Secondly, we focus on a detailed study of mobility generators, network, and vehicular ad hoc network simulators, as well as the available vehicular data sets. We thus provide an overarching view of the complete domain of vehicular cloud computing and identify areas for future research directions.

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Section: Storage As a Servicementioning

confidence: 99%

Services and simulation frameworks for vehicular cloud computing: a contemporary survey

Ahmed

Malik

Hafeez

et al. 2019

J Wireless Com Network

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“…4. To calculate delivery rate, (4) is used for the leader probability and (10)- (12) have been used for the probability of leader transmissions, then the differential knowledge probabilities are given by (13), (14) and the delivery rate is given by (3). In all settings, it is observed that the users in group H have reached the delivery rate of λ, and for the users in L, there is a reasonable match between the simulation and analytical results.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Assuming that these packets are uniformly distributed, we have: Table III. In calculation of the delivery rate, β h is given by (4), differential knowledges are given by (13) and (14) and the delivery rate is given by (3).…”

Section: Delivery Ratementioning

confidence: 99%

Multirate Packet Delivery in Heterogeneous Broadcast Networks

Zarrabian

Tabataba

Molavipour

et al. 2019

IEEE Trans. Veh. Technol.

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In heterogeneous packet erasure networks, where the capacities of the channels are different, it is important that each user can receive packets with respect to the quality of its channel, which is known as multirate packet delivery. In this paper, we provide an in-depth analysis of a previously proposed non-block-based network coding scheme in the literature and show analytically and via simulations for the first time, that it can achieve multirate packet delivery. Using acknowledgments from each user, the user with the highest link capacity achieves the maximum possible throughput. Also, a non-zero packet delivery rate is possible for the other users, which depends on the difference between the packet arrival rate at the sender and the link capacity of each user. We present an analysis of the delivery rate and the delivery delay of the users. The accuracy of the analysis is confirmed by comparing the results with simulations for different settings of packet arrival rate at the sender and the link capacities.

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“…In this scenario, Qiao et al determine which bandwidth to allocate to a vehicle entering in a 5G cell. In [26], Guo et al consider a two time-scale dynamic caching solution. They address the cache placement (by determining whether the video should be cached or not), the video quality adaptation at the application layer and the selection of the physical transmission range.…”

Section: Related Workmentioning

confidence: 99%

Performance analysis of video on demand in an IEEE 802.11p-based vehicular network

Begin

Busson

Lassous

et al. 2019

Computer Communications

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We consider a VoD (Video on-Demand) platform designed for vehicles traveling on a highway or other major roadway. Typically, cars or buses would subscribe to this delivery service so that their passengers get access to a catalog of movies and series stored on a back-end server. The network infrastructure comprises IEEE 802.11p RSUs (Road Side Units) that are deployed along the highway and deliver video content to traveling vehicles. In this paper, we propose a simple analytical and yet accurate solution to estimate two key performance parameters for a VoD platform: (i) the average download data rate experienced by vehicles over their journey and (ii) the average "interruption time", which corresponds to the fraction of time the video playback of a given vehicle is interrupted because of an empty buffer. Through multiple examples, we investigate the influence of several parameters (e.g., the video bit rate, the number of vehicles, the distance between RSUs, the vehicle velocity) on these two performance parameters whose outcome may help the sizing of an IEEE 802.11p-based VoD platform.

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Cache-Enabled Adaptive Video Streaming Over Vehicular Networks: A Dynamic Approach

Cited by 75 publications

References 32 publications

Services and simulation frameworks for vehicular cloud computing: a contemporary survey

Services and simulation frameworks for vehicular cloud computing: a contemporary survey

Multirate Packet Delivery in Heterogeneous Broadcast Networks

Performance analysis of video on demand in an IEEE 802.11p-based vehicular network

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