Design and Performance Evaluation of Network-assisted Control Strategies for HTTP Adaptive Streaming

Abstract

This article investigates several network-assisted streaming approaches that rely on active cooperation between video streaming applications and the network. We build a Video Control Plane that enforces Video Quality Fairness among concurrent video flows generated by heterogeneous client devices. For this purpose, a max-min fairness optimization problem is solved at runtime. We compare two approaches to actuate the optimal solution in an Software Defined Networking network: The first one allocates network bandwidth slices to video flows, and the second one guides video players in the video bitrate selection. We assess performance through several QoE-related metrics, such as Video Quality Fairness, video quality, and switching frequency. The impact of client-side adaptation algorithms is also investigated.

Optimal Network-Assisted Multiuser DASH Video Streaming

“…From this perspective, our main intention with NS-3 simulations in this part of the paper is to illustrate the optimality gap between the BDRA algorithm and other selected rate-fair resource allocation schemes. As a result, with this intention in the paper, we only compare the performance of the BDRA algorithm with other potential resource allocation mechanisms whose operation does not require knowledge about either network throughput rates, or channel quality indicators, or detailed client operation as different from most existing work in the literature [15], [16], [31]- [37].…”

“…In [53] and [54], the authors have identified three main performance issues: (i) player instability, (ii) unfairness between the players and (iii) the under-utilization of the available bandwidth. It is pointed out that the main cause of these [31], [32], [34], [59] × × × × × [37], [35] ×…”

“…On the other hand, the BDRA scheme we derive in this paper operates without requiring any such information at the server-side and minimizes the system-wide stalling probability under such no-feedback conditions. Last but not least, there are also other recent papers that customize the software-defined-networking (SDN) architecture for the HTTP adaptive streaming scenarios in order to have a central controller helping users to select the optimal video quality level and helping the access point to share available resources among the users intelligently [15], [35]- [37]. In [15], the authors propose a guidance mechanism for distributing HTTP-GET requests over time in order to improve QoE in HTTP video streaming systems.…”

Video Caching, Analytics, and Delivery at the Wireless Edge: A Survey and Future Directions

“…A number of recent works have studied SAND-DASH in such a context. The authors in [79] design rate-control strategies, such as bandwidth reservation and bitrate guidance, by using edge controllers for SAND-DASH. They also evaluate the proposed streaming schemes in terms of the average video quality received at multiple clients and the fairness in video delivery.…”

Bandwidth Prediction Schemes for Defining Bitrate Levels in SDN-enabled Adaptive Streaming

“…Authors in [11] design a QoEbased SDN architecture that considers the cooperation between network operators and content providers. Further, Cofano et al, [12] investigated several network-assisted streaming strategies that allow active cooperation between the video streaming applications and the network.…”