Quality-Aware Streaming and Scheduling for Device-to-Device Video Delivery

Abstract

On-demand video streaming is becoming a killer application for wireless networks. Recent information-theoretic results have shown that a combination of caching on the users' devices and device-to-device (D2D) communications yields throughput scalability for very dense networks, which represent critical bottlenecks for conventional cellular and wireless local area network (WLAN) technologies. In this paper, we consider the implementation of such caching D2D systems where each device pre-caches a subset of video files from a library, and users requesting a file that is not already in their library obtain it from neighboring devices through D2D communication. We develop centralized and distributed algorithms for the delivery phase, encompassing a link scheduling and a streaming component. The centralized scheduling is based on the max-weighted independent set (MWIS) principle and uses message-passing to determine max-weight independent sets. The distributed scheduling is based on a variant of the FlashLinQ link scheduling algorithm, enhanced by introducing video-streaming specific weights. In both cases, the streaming component is based on a quality-aware stochastic optimization approach, reminiscent of current DynamicAdaptive Streaming over HTTP (DASH) technology, for which users sequentially request video "chunks" by choosing adaptively their quality level. The streaming and the scheduling components are coupled by the length of the users' request queues. Through extensive system simulation, the proposed approaches are shown to provide sizeable gains with respect to baseline schemes formed by the concatenation of off-the-shelf FlashLinQ with proportional fair link scheduling and DASH at the application layer.

Slack time–based scheduling scheme for live video streaming in P2P network

“…Pre‐caches approach can also improve the performance of scheduling scheme in a P2P network. An approach is described by Kim et al, using the HTTP (DASH) technology for improving the overall scheduling scheme using the re‐caches approach …”

Energy-Efficient Dynamic Packet Downloading for Medical IoT Platforms

“…The time-varying dynamic control algorithms for utility/delay tradeoffs are also discussed in [24]- [26]. The proposed algorithms in [24]- [26] have constant tradeoff coefficients which determine the relationship between utilities and delays.…”

“…The proposed algorithms in [24]- [26] have constant tradeoff coefficients which determine the relationship between utilities and delays. However, the proposed scheme in this paper makes the coefficient as a variable (i.e., V (p i [t]) which is defined in 21) which depends on the residual power/energy in each AP, i.e., the proposed algorithm in this paper is adaptive in terms of energy-efficiency (more details are in Section III-A).…”

“…The proposed algorithm in this paper considers propagation loss according to the measurement-based standardized 60 GHz path-loss models. In [26], a stochastic control algorithm was designed for the tradeoffs between video-qualities and delays in device-todevice video streaming. However, the proposed algorithm in [26] does not consider energy-efficiency which is the major objective of the proposed stochastic control algorithm in this paper.…”

“…To make a power allocation decision P TX i [t] in a i ∈ A for minimizing (26), the (26) should be differentiated by P TX i [t] and set to 0. Then the minimum solution of P TX i [t] in each AP ∀a i ∈ A can be obtained as follows:…”