mCast: An SDN-Based Resource-Efficient Live Video Streaming Architecture with ISP-CDN Collaboration

AC-SDVN: An Access Control Protocol for Video Multicast in Software Defined Vehicular Networks

“…Based on the above analysis, the realization of efficient and flexible vehicular video multicast is inseparable from the support of the 5G network and its fundamental enabler SDN [11], [16], [17], [18]. And there have no corresponding solutions to the security problems.…”

“…Therefore, when designing the access control scheme for SDVN, we refer to the design like Khalid et al's [18] and Fiat et al's [49]. By deploying the authentication module in the SDN controller and supporting batch verification, realize the efficient authentication of large-scale vehicles and unsupervised RSUs.…”

“…Existing research shows that the introduction of SDN into multicast applications in vehicular networks can achieve the following three main advantages: 1) simplify the network management of multicast applications and improve the flexibility of vehicular networks [16]; 2) make effective routing decisions and reduce network burden by using the global network information collected from the roadside units (RSUs) and vehicles [17]; 3) improve data distribution efficiency and provide a better quality of service in vehicular networks [18]. But, to realize the large-scale application of video multicast in software defined vehicular networks (SDVN), it is necessary to provide access control mechanisms [19].…”

A Contemporary Survey on Live Video Streaming from a Computation-Driven Perspective

“…Although the multicast (mCast) Internet protocol is beneicial for reducing resource consumption because a stream in LVS mCast is delivered to a group of clients simultaneously in a single transmission attempt, it has a static and rigid nature when mCast is used separately [65]. Motivated by resolving this drawback, in [101], the software-deined networking (SDN) architecture is cooperative with the LVS mCast approach, where mCast has proven its capability of more than 50% link utilization improvement and 0% network losses, leading to a degradation in bandwidth consumption. Further consideration [175] was made to show the additional integration among the network function virtualization (NFV), SDN, and mCast in various beneicial network applications, including online conferencing, LVS, event monitoring, etc., from which the network throughput was maximized while minimizing the computing and bandwidth resource consumption.…”

“…Massive connectivity has been considered one of the major requirements for realizing future communication networks, where billions of user devices participate in the Internet to exchange information [172]. As video traic Hit Ratio [117] Edge, ABS using FoV-aware Increased hit ratio by at least 40% and 17% compared with LFU and LRU, respectively [119] Smart edge caching Performance in terms of hit ratio was better than LFU, LRU, and FIFO [183] Edge, SVC, SPLF caching Achievable hit ratio outperformed LFU, LRU, and GDSF [26] A novelty caching solution Maximize the cache hit ratio under the constraints of the storage capacity and manifested the outperformance compared with LFU, LRU, and weighted GDSF [187] MUFC caching Gained better hit ratio than advanced FIFO caching and FairRide caching [139] LCC caching Signiicantly improved hit ratio value compared with independent and femto-caching [34] Edge, ABS using HITCOT Indicates the tradeof between the hit ratio and content quality Resource Consumption [169,190] Edge/Fog architectures Signiicantly reduced bandwidth consumption in a crowded network [188] Edge computing Gained efectiveness of low computing consumption, low latency, and high video quality [144] Edge/fog architectures Obtained the degradation of 7% computing load, 27.3% caching memory usage, 3.6% energy consumption, up to 33% backhaul, and 5% fronthaul communication bandwidth [47] ML-based viewport prediction Further saves bandwidth resources [11] ML-based resource allocation Energy eiciency approach to achieve substantial energy savings [58] DNN-based MCDNN Efective degradation of caching and energy consumption satisied the low-latency stringent requirement under the constraints of the given computing accuracy [96] DNN-based Chameleon 30ś50 % computing resource improvement and 20ś50 % higher computing accuracy [101] SDN and mCast Improved 50% link utilization achieved 0% network losses, leading to bandwidth savings [175] NFV, SDN, and mCast Maximized network throughput and minimized computing and bandwidth consumption [176] SDN, mCast, and scalable ABS Conirmed equivalent bandwidth efectiveness [40] Edge, mCast, and ABS Gained bandwidth and energy resource eiciency, ensured fairness and live capability [7] Hybrid unicast and mCast Provided high network load, low energy consumption, outperformed in the hit ratio, spectral eiciency, video quality, frame loss rate, bufering delay, re-bufering number [106] ...…”