21 research outputs found
Traffic Profiling for Mobile Video Streaming
This paper describes a novel system that provides key parameters of HTTP
Adaptive Streaming (HAS) sessions to the lower layers of the protocol stack. A
non-intrusive traffic profiling solution is proposed that observes packet flows
at the transmit queue of base stations, edge-routers, or gateways. By analyzing
IP flows in real time, the presented scheme identifies different phases of an
HAS session and estimates important application-layer parameters, such as
play-back buffer state and video encoding rate. The introduced estimators only
use IP-layer information, do not require standardization and work even with
traffic that is encrypted via Transport Layer Security (TLS). Experimental
results for a popular video streaming service clearly verify the high accuracy
of the proposed solution. Traffic profiling, thus, provides a valuable
alternative to cross-layer signaling and Deep Packet Inspection (DPI) in order
to perform efficient network optimization for video streaming.Comment: 7 pages, 11 figures. Accepted for publication in the proceedings of
IEEE ICC'1
Customized Packet Scheduling Algorithm for LTE Network
Advanced mobile networks are expected to provide omnipresent broadband access to a continuously growing number of mobile users. LTE system represents 4G mobile network. The key feature thereof is the adoption of advanced Radio Resource Management procedures in order to increase the system performance up to Shannon’s limit. Packet scheduling mechanisms, in particular, play a fundamental role, because they are responsible for choosing, with fine time and frequency resolutions, how to distribute scarce radio resources among different mobile stations, taking into account channel conditions and QoS requirements. This objective should be accomplished by providing an optimal trade-off between spectral efficiency and fairness. In this context, this paper proposes customized packet scheduling algorithm designed to adaptively alter scheduling schemes considering multiple input variables in order to maximize spectral efficiency as well as overall system performance
Price-Based Controller for Utility-Aware HTTP Adaptive Streaming
HTTP Adaptive Streaming (HAS) permits to efficiently deliver video to multiple heterogenous
users in a fully distributed way. This might however lead to unfair bandwidth utilization among
HAS users. Therefore, network-assisted HAS systems have been proposed where network elements
operate alongside with the clients adaptation logic for improving users satisfaction. However,
current solutions rely on the assumption that network elements have full knowledge of the network
status, which is not always realistic. In this work, we rather propose a practical network-assisted
HAS system where the network elements infer the network link congestion using measurements
collected from the client endpoints, the congestion level signal is then used by the clients to
optimize their video data requests. Our novel controller maximizes the overall users satisfaction
and the clients share the available bandwidth fairly from a utility perspective, as demonstrated
by simulation results obtained on a network simulator
Random Linear Network Coding for 5G Mobile Video Delivery
An exponential increase in mobile video delivery will continue with the
demand for higher resolution, multi-view and large-scale multicast video
services. Novel fifth generation (5G) 3GPP New Radio (NR) standard will bring a
number of new opportunities for optimizing video delivery across both 5G core
and radio access networks. One of the promising approaches for video quality
adaptation, throughput enhancement and erasure protection is the use of
packet-level random linear network coding (RLNC). In this review paper, we
discuss the integration of RLNC into the 5G NR standard, building upon the
ideas and opportunities identified in 4G LTE. We explicitly identify and
discuss in detail novel 5G NR features that provide support for RLNC-based
video delivery in 5G, thus pointing out to the promising avenues for future
research.Comment: Invited paper for Special Issue "Network and Rateless Coding for
Video Streaming" - MDPI Informatio
Improving mobile video quality through predictive channel quality based buffering
Frequent variations in throughput make mobile networks a challenging environment for video streaming. Current video players deal with those variations by matching video quality to network throughput. However, this adaptation strategy results in frequent changes of video resolution and bitrate, which negatively impacts the users' streaming experience. Alternatively, keeping the video quality constant would improve the experience, but puts additional demand on the network. Downloading high quality content when channel quality is low requires additional resources, because data transfer efficiency is linked to channel quality. In this paper, we present a predictive Channel Quality based Buffering Strategy (CQBS) that lets the video buffer grow when channel quality is good, and relies on this buffer when channel quality decreases. Our strategy is the outcome of a Markov Decision Process. The underlying Markov chain is conditioned on 377 real-world LTE channel quality traces that we have collected using an Android mobile application. With our strategy, mobile network providers can deliver constant quality video streams, using less network resources
Price-based Controller for Quality-Fair HTTP Adaptive Streaming
HTTP adaptive streaming (HAS) has become the universal technology for video streaming over the Internet. Many HAS system designs aim at sharing the network bandwidth in a rate-fair manner. However, rate fairness is in general not equivalent to quality fairness as different video sequences might have different characteristics and resource requirements. In this work, we focus on this limitation and propose a novel controller for HAS clients that is able to reach quality fairness while preserving the main characteristics of HAS systems and with a limited support from the network devices. In particular, we adopt a price-based mechanism in order to build a controller that maximizes the aggregate video quality for a set of HAS clients that share a common bottleneck. When network resources are scarce, the clients with simple video sequences reduce the requested bitrate in favor of users that subscribe to more complex video sequences, leading to a more efficient network usage. The proposed controller has been implemented in a network simulator, and the simulation results demonstrate its ability to share the available bandwidth among the HAS users in a quality-fair manner