Revisiting multimedia streaming in mobile ad hoc networks

Mobile ad hoc networks have been the subject of active re-search for a number of years. This paper investigates the feasibility of using such networks for transmitting multime-dia streams. We observe that wireless network IO operations can be expensive (e.g., programmed IO cost, energy to op-erate wireless). Moreover, compared to nodes in infrastruc-ture networks that either read or write network traffic, ad hoc traffic requires the intermediate node to perform many expensive network operations twice (read and then resend) and on behalf of other nodes. This observation raises an im-portant question for the ad hoc community, should they a) demand that ad hoc routers support some minimum hard-ware resources (for example, full DMA support, twice the battery capacity)?, b) force an end-to-end resource manage-ment scheme that cooperatively reduces the network flow to half of what can be serviced by the weakest link? This would ensure that no intermediate node would see enough traffic to overwhelm them? or c) require that the local nodes protect themselves from transit traffic? This paper explores the last mechanism in order to provide some control over the resource consumed without a major revamp of existing operating systems or requiring special hardware. We im-plement our mechanism in the network driver and present encouraging preliminary results

Exploiting the power of multiplicity: a holistic survey of network-layer multipath

The Internet is inherently a multipath network: For an underlying network with only a single path, connecting various nodes would have been debilitatingly fragile. Unfortunately, traditional Internet technologies have been designed around the restrictive assumption of a single working path between a source and a destination. The lack of native multipath support constrains network performance even as the underlying network is richly connected and has redundant multiple paths. Computer networks can exploit the power of multiplicity, through which a diverse collection of paths is resource pooled as a single resource, to unlock the inherent redundancy of the Internet. This opens up a new vista of opportunities, promising increased throughput (through concurrent usage of multiple paths) and increased reliability and fault tolerance (through the use of multiple paths in backup/redundant arrangements). There are many emerging trends in networking that signify that the Internet's future will be multipath, including the use of multipath technology in data center computing; the ready availability of multiple heterogeneous radio interfaces in wireless (such as Wi-Fi and cellular) in wireless devices; ubiquity of mobile devices that are multihomed with heterogeneous access networks; and the development and standardization of multipath transport protocols such as multipath TCP. The aim of this paper is to provide a comprehensive survey of the literature on network-layer multipath solutions. We will present a detailed investigation of two important design issues, namely, the control plane problem of how to compute and select the routes and the data plane problem of how to split the flow on the computed paths. The main contribution of this paper is a systematic articulation of the main design issues in network-layer multipath routing along with a broad-ranging survey of the vast literature on network-layer multipathing. We also highlight open issues and identify directions for future work

Research challenges in 5G networks: a HetNets perspective

This paper highlights use cases, emerging machine type communication (MTC) technologies, ongoing research activities, and existing research challenges in 5G networks. 5G networks are faced with the following challenges: (i) handling large amounts of data, (ii) coping with different types of data traffic, i.e., human-type, machine-type, and combined-type (iii) connecting billions of machines, and (iv) severe resource limitations of devices. The ubiquitous nature of cellular networks make them the preferred choice for access networks, but a lack of communication resources is a problem. To address the resource scarcity issue, different wireless access networks may combine to form a heterogeneous network (HetNet) and hence become a single 5G network. For long-term success of 5G networks, we envision the following as important research outputs: (i) a scalable 5G network architecture that can handle a large number of human users and machines considering different constraints, (ii) a comprehensive quality of service (QoS) framework to satisfy heterogeneous users and machines requirements, (iii) a procedure for intelligent access network selection, and (iv) comprehensive inter-network handover mechanisms

RL-OPRA: Reinforcement Learning for Online and Proactive Resource Allocation of crowdsourced live videos

© 2020 Elsevier B.V. With the advancement of rich media generating devices, the proliferation of live Content Providers (CP), and the availability of convenient internet access, crowdsourced live streaming services have witnessed unexpected growth. To ensure a better Quality of Experience (QoE), higher availability, and lower costs, large live streaming CPs are migrating their services to geo-distributed cloud infrastructure. However, because of the dynamics of live broadcasting and the wide geo-distribution of viewers and broadcasters, it is still challenging to satisfy all requests with reasonable resources. To overcome this challenge, we introduce in this paper a prediction driven approach that estimates the potential number of viewers near different cloud sites at the instant of broadcasting. This online and instant prediction of distributed popularity distinguishes our work from previous efforts that provision constant resources or alter their allocation as the popularity of the content changes. Based on the derived predictions, we formulate an Integer-Linear Program (ILP) to proactively and dynamically choose the right data center to allocate exact resources and serve potential viewers, while minimizing the perceived delays. As the optimization is not adequate for online serving, we propose a real-time approach based on Reinforcement Learning (RL), namely RL-OPRA, which adaptively learns to optimize the allocation and serving decisions by interacting with the network environment. Extensive simulation and comparison with the ILP have shown that our RL-based approach is able to present optimal results compared to heuristic-based approaches.This work was supported by the Qatar Foundation