Backhaul-Aware Caching Placement for Wireless Networks

As the capacity demand of mobile applications keeps increasing, the backhaul network is becoming a bottleneck to support high quality of experience (QoE) in next-generation wireless networks. Content caching at base stations (BSs) is a promising approach to alleviate the backhaul burden and reduce user-perceived latency. In this paper, we consider a wireless caching network where all the BSs are connected to a central controller via backhaul links. In such a network, users can obtain the required data from candidate BSs if the data are pre-cached. Otherwise, the user data need to be first retrieved from the central controller to local BSs, which introduces extra delay over the backhaul. In order to reduce the download delay, the caching placement strategy needs to be optimized. We formulate such a design problem as the minimization of the average download delay over user requests, subject to the caching capacity constraint of each BS. Different from existing works, our model takes BS cooperation in the radio access into consideration and is fully aware of the propagation delay on the backhaul links. The design problem is a mixed integer programming problem and is highly complicated, and thus we relax the problem and propose a low-complexity algorithm. Simulation results will show that the proposed algorithm can effectively determine the near-optimal caching placement and provide significant performance gains over conventional caching placement strategies.Comment: 6 pages, 3 figures, accepted to IEEE Globecom, San Diego, CA, Dec. 201

Cooperative video transmission strategies via caching in small-cell networks

Small-cell network is a promising solution to the high video traffic. However, it has some fundamental problems, i.e., high backhaul cost, quality of experience (QoE) and interference. To address these issues, we propose a cooperative transmission strategy for video transmission in small-cell networks with caching. In the scheme, each video file is encoded into segments using a maximum distance separable rateless code. Then, a portion of each segment is cached at a certain small-cell base station (SBS), so that the SBSs can cooperatively transmit these segments to users without incurring high backhaul cost. When there is only one active user in the network, a greedy algorithm is utilized to deliver the video-file segment from the SBS with good channel state to the user watching videos in real time. This reduces video freezes and improves the QoE. When there exist several active users, interference will appear among them. To deal with interference, interference alignment (IA) is adopted. Based on the scheme for a single user, the greedy algorithm and IA are combined to transmit video-file segments to these users, and the performance of the system can be significantly improved. Simulation results are presented to show the effectiveness of the proposed scheme

Quality of experience-centric management of adaptive video streaming services : status and challenges

Video streaming applications currently dominate Internet traffic. Particularly, HTTP Adaptive Streaming ( HAS) has emerged as the dominant standard for streaming videos over the best-effort Internet, thanks to its capability of matching the video quality to the available network resources. In HAS, the video client is equipped with a heuristic that dynamically decides the most suitable quality to stream the content, based on information such as the perceived network bandwidth or the video player buffer status. The goal of this heuristic is to optimize the quality as perceived by the user, the so-called Quality of Experience (QoE). Despite the many advantages brought by the adaptive streaming principle, optimizing users' QoE is far from trivial. Current heuristics are still suboptimal when sudden bandwidth drops occur, especially in wireless environments, thus leading to freezes in the video playout, the main factor influencing users' QoE. This issue is aggravated in case of live events, where the player buffer has to be kept as small as possible in order to reduce the playout delay between the user and the live signal. In light of the above, in recent years, several works have been proposed with the aim of extending the classical purely client-based structure of adaptive video streaming, in order to fully optimize users' QoE. In this article, a survey is presented of research works on this topic together with a classification based on where the optimization takes place. This classification goes beyond client-based heuristics to investigate the usage of server-and network-assisted architectures and of new application and transport layer protocols. In addition, we outline the major challenges currently arising in the field of multimedia delivery, which are going to be of extreme relevance in future years

Mobile Content Delivery Network Design and Implementation

In this thesis, a novel concept of Mobile Content Delivery Network is designed and implemented in a real testbed with the target of flexibly adapting the video caching in the cellular network to the users dynamics. New challenges are discussed and practical considerations for wide-scale deployment in next generation cellular networks are drawn

Cloud-Based Mobile Video Streaming Techniques

Reasoning processing is changing the landscape of the electronic digital multi-media market by moving the end customers concentrate from possession of video to buying entry to them in the form of on-demand delivery solutions At the same time the cloud is also being used to store possessed video paths and create solutions that help audience to discover a whole new range of multi-media Cellular devices are a key car owner of this change due to their natural mobility and exclusively high transmission rate among end customers This document investigates cloud centered video streaming methods particularly from the mobile viewpoint The qualitative part of the research contains explanations of current video development methods streaming methods and third celebration cloud centered streaming solutions for different mobile which shows my realistic work relevant to streaming methods with RTMP protocols family and solutions for iPhone Android Smart mobile phones Window and BalackBerry phones et

Wireless networks QoS optimization using coded caching and machine learning algorithms

Proactive caching shows great potential to minimize peak traffic rates by storing popular data, in advance, at different nodes in the network. We study three new angles of proactive caching that were not covered before in the literature. We develop more practical algorithms that bring proactive caching closer to practical wireless networks. The first angle is where the popularities of the cached files are changing over time and the file delivery is asynchronous. We provide an algorithm that minimizes files’ delivery rate under this setting. We show that we can use the file delivery messages to proactively and constantly update the receiver finite caches. We show that this mechanism reduces the downloaded traffic of the network. The proposed scheme uses index coding [1], and app. A to jointly encodes the delivery of different demanded files with the cache updates to other receivers to follow the changes in the file popularities. An offline and online (dynamic) versions of the scheme are proposed, where the offline version requires knowledge of the file popularities across the whole transmission period in advance and the online one requires the file popularities for one succeeding time slot only. The optimal caching for both the offline and online schemes is obtained numerically. The second angle is the study of segmented caching for delay minimization in networks with congested backhaul. Studies have mainly focused on proactively storing popular whole files. For certain categories of files like videos, this is not the best strategy. As videos can be segmented, sending later segments of videos can be less time-critical. Video is expected to constitute 82% of internet traffic by 2020 [2]. We study the effect of segmenting video caching decisions under the assumption that the backhaul is congested. We provide an algorithm for proactive segmented caching that optimizes the choice of segments to be cached to minimize delay and compare the performance to the whole file proactive caching. The third angle focuses on using reinforcement learning for coded caching in networks with changing file popularities. For such a dynamic environment, reinforcement learning has the flexibility to learn the environment and adapt accordingly. We develop a reinforcement learning-based coded caching algorithm and compare its performance to rule-based coded caching

Efficient Algorithms for Cache-Throughput Analysis in Cellular-D2D 5G Networks

In this paper, we propose a two-tiered segment-based Device-to-Device (S-D2D) caching approach to decrease the start up and playback delay experienced by Video-on-Demand (VoD) users in a cellular network. In the S-D2D caching approach cache space of each mobile device is divided into two cache-blocks. The first cache-block reserve for caching and delivering the beginning portion of the most popular video les and the second cache-block caches the latter portion of the requested video les ‘fully or partially’ depending on the users’ video watching behaviour and popularity of videos. In this approach before caching, video is divided and grouped in a sequence of fixed-sized fragments called segments. To control the admission to both cache-blocks and improve the system throughput, we further propose and evaluate three cache admission control algorithms. We also propose a video segment access protocol to elaborate on how to cache and share the video segments in a segmentation based D2D caching architecture. We formulate an optimisation problem and the optimal cache probability and beginning-segment size that maximise the cache-throughput probability of beginning-segments. To solve the non-convex cache-throughout maximisation problem, we derive an iterative algorithm, where the optimal solution is derived in each step. We used extensive simulations to evaluate the performance of our proposed S-D2D caching system