In this paper, we propose a two-tiered segment-based Device-to-Device (S-D2D) caching approach to decrease the startup 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 files and the second cache-block caches the latter portion of the requested video files ‘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 find 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.
Mobile communication has gained tremendous popularity over the last decade due to the evolution toward higher generation (G) cellular networks from 1G to 5G. Specifically, the transition to 3G and the successful deployment of 4G technology was a boom in mobile data consumption. Due to the increase in mobile broadband speed, now millions of mobile users are watching videos on their smart mobile devices. According to the latest Ericsson mobility report, mobile video traffic is forecast to account for 74% of all mobile data traffic in 2024 [
To confront growing mobile data traffic demands, many technologies have been explored and developed in the last decade, such as massive MIMO (deploying very dense and massive base station (BS) antennas) [
Recently, D2D caching network has become the centre of attention of both academia and industry research. D2D communication allows direct communication between proximal devices without traversal data through the BS or core network and without requiring additional infrastructure and maintenance cost. The D2D caching system works on the following mechanism: each device with storage capacity caches a subset of video files at random according to some popularity distribution. When a user requests video content, the individual might either find the desired video file in the local cache or will download from a proximity device through the direct D2D link [
Many studies have shown that dissemination of popular video content through the D2D network can improve spectrum utilisation, video throughput, energy efficiency, and user’s experience. For instance, in [
Interestingly, Golrezaei et al. [
The users abandoned the videos due to a variety of reasons such as lack of interest, a multitude of choices in video content, frequent re-buffering, length of video content, and long startup delays. For instance, authors in [
Under such circumstances, due to heterogeneous and limited users’ cache resources
Based on the observations mentioned above and different from the existing D2D caching algorithms [ We proposed a two-tiered S-D2D caching approach by taking into considerations video popularity and users’ video abandonment behaviour. Our simulation results show that by employing S-D2D caching approach, the VoD users in a cellular network can experience a decrease in startup-time and playback-delay. 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. To control the admission of video segments to both blocks of cache, we propose the (a) Beginning-Segments Cache Policy (BSCP), (b) Selective Partial Cache Policy (SPCP), and (c) Short Length Video Cache Policy (SLVCP). We propose these caching algorithms based on the findings of an extensive statistical studies which had been conducted to analyse the video viewing behaviour of millions of VoD users. To the best of our knowledge, none of the existing work has examined the effectiveness of the segmentation-based partial caching approach for large and small videos in a D2D communication scenario. We derive an optimisation approach in a stochastic D2D caching scenario to maximise the cache-throughput probability of the beginning-segments. The cache-throughput probability is the sum of successful requests served by the local caches (self-hit probability) of the requesting device and through the D2D link (cache-hit probability). We take into consideration the size of the beginning-segments and realistic network characteristics such as interference, shadowing, and success probability. To solve the non-convex cache-throughout maximisation problem, we derive an iterative algorithm, where the optimal solution found in each step. Finally, the admission control algorithms are evaluated and compared through numerical simulations in a realistic channel model, based on a practical indoor-hotspot WINNER-II propagation environment [
The remainder of the article is structured as follows: Section 2 discusses the proposed two-tiered S-D2D caching approach in detail. Sections 3–5 elaborate the BSCP, SPCP and SLVCP. The system model is presented in Section 6. Section 7 presents the simulation results which illustrate the performance of the proposed caching policies in terms of the average cache-hit ratio, average self-hit ratio, and average cache-throughput ratio. Finally, Section 8 concludes the whole article.
In this section, we first discuss our proposed segmentation strategy for the video file and the user’s cache space. Then, the video segment access protocol in an S-D2D caching network discussed in detail. Finally, we turn our discussion to the caching policies.
The segmentation strategy we use for the video files in an S-D2D caching architecture is illustrated in
The video file j is sliced into small pieces of equal size transmit units (TUs) before it transmitted to the end-user. The TU is the basic building block of information transmission in a communication system. Depending on the cache system requirements, multiple TUs grouped into equal-sized segments. The number of TUs grouped in each segment
We divide the users’ cache space into two blocks of different sizes, for example, see
Each D2D device in the S-D2D caching system has a storage capacity that enables two proximity devices to cache and share the video segments over a direct link. We assume that a cellular user, willing to distribute and receive the video segments via an S-D2D caching system, must first install special software—we named it “D2D service interface software (D-SIS)”. The D-SIS implemented as a background application software that runs when the cellular user is logged into his/her system and requests video content. The main objective of the D-SIS is to make the cache placement, video segmentation and communication process between a pair of devices transparent and reliable. The proposed S-D2D caching system is operator/network controlled.
When a cellular user requests a video file, the D-SIS first performs a self-search process before placing the request to the BS. We termed it as a self-hit. For a fast startup, the D-SIS searches Block-1 of the user’s cache space reserved for caching only the beginning-segments. The video will start with zero startup-time if the user has cached the beginning-segments of the desired content in its local storage. In this case, if the self-search process is unsuccessful, the D-SIS contacts the core network for the list of potential D2D (P-D2D) devices. The P-D2D device has the copy of the desired video segments in its local storage and located nearby of the requesting device. The core network filters the information stored in its central database
Unluckily, suppose for some reason such as bad link quality, the requesting device is unable to download the segments from the neighbouring P-D2D, it will either start to download the segments from one of the P-D2D devices provided in a list, or the BS must serve the request. Similarly, in a case, the core network is unable to provide the desired list of P-D2D devices; it will forward the beginning-segments to the requesting device through the traditional cellular communication system. For service continuity and downloading the subsequent segments, the D-SIS will repeat the same procedure as discussed above and will explore the Block-2 of the user’s cache space dedicated to caching the later segments of the video files.
Beginning-segments always receive preferential treatment in the S-D2D caching approach, and video always starts with low startup latency. Algorithm 1 illustrates the BSCP for the admission as well as the replacement of the beginning-segments.
In general, cellular users request video content according to some popularity distribution. Many studies have reported the skewed distribution of users’ interest toward a small fraction of top-ranked content. For instance, authors in [
We assume that a cellular user requests a segment i of a video file j from the library of size L. We also assume that the video and its segments share the same popularity distribution. The popularity of the requested segment ‘i’ of the video file ‘j’ is denoted by
where
One of the most straightforward approaches for caching the later segments of a large video file in Block-2, we keep on caching the subsequent segments immediately after the beginning-segments until the cache is full. However, to achieve higher throughput, this approach may not be feasible for the D2D caching system. For instance, a two-hour-long YouTube 1080p video file consumes 7.36 GB cache capacity. It is more likely that for a few popular video files such as top 20 movie content users will watch the content in its entirety. For less popular content a cellular user may abandon an ongoing video session before its completion. For instance, a comparison between the abandon rate of mobile and fixed-line users has conducted in a study of BBC iPlayer accesses. The results suggested that mobile users abandon sessions with a higher rate, i.e., only around 30% of mobile sessions last for longer than a half of a content’s duration in comparison to around 50% for the fixed-line sessions [
Based on the measurement studies [
When cache space required for the incoming new video file, the video chunk of the least popular video file is evicted to make room for the video chunks of the most popular video files.
Many studies have reported that shorter videos have a longer viewing time and the abandonment rate increases as the video length increases. For instance, the authors in [
A similar observation is reported in a study [ As video abandonment rate for small size videos is low in comparison to large size videos; therefore, for small size videos (3–5 min long), we propose to cache the whole video file. For clarity, However, like the BSCP, only the subsequent segments of the most popular videos can cache on Block-2. The Zipf distribution model will be used to measure the popularity of short length multimedia files. In short, we follow the same cache policy as described in Algorithm-1 for caching and replacing the later segments of the small video files into the Block-2.
In this section, we derive optimal cache probability and optimal beginning-segment size for the BSCP that maximises the cache-throughput probability. First, we discuss the system model. Then, the optimisation problem is formulated, and a solution is derived.
In this section, we derive optimal cache probability and optimal beginning-segment size for the BSCP that maximises the cache-throughput probability. First, we discuss the system model. Then, the optimisation
Each P-D2D has a probability
Each P-D2D device in the system model can communicate with each other over a single direct link sing the cellular spectrum resources, as well as with the BSs through a traditional cellular communication system. Since the distance between P-D2D devices is typically small, multiple D2D active links can exist throughout the cellular region. Furthermore, we will also consider the interference received at each P-D2D receiver caused by the powerful signals transmitted by the BSs and other active D2D links within and outside the cell. For simplicity, we also assume that all P-D2D devices and BSs use the same transmission power. The transmission power determines the actual transmission range and can be optimised centrally. Typically, there is a trade-off between the transmission power and the probability of availability of the P-D2D devices caching the requested video segments. It indicates that higher transmission power leads to higher transmission coverage area and hence, increases the probability of finding the requested video-segments within the vicinity of the requesting users [
Next, we derive the optimal beginning-segment size
Averaging over all the beginning-segments of the video files in a content library L, we have the D2D cache-hit probability as follows
Thus,
Here, we are mainly interested in optimising the size of the beginning-segment and the cache probability that increase the average number of requests that can be successfully and simultaneously handled by the P-D2D devices per unit area. In the self-hit probability case, the request automatically served with probability one. In the cache-hit probability case, the success probability of content delivery depends on the received signal-to-interference-plus-noise ratio (SINR). Thus, we have the cache-throughput as follows:
where
The
where
As the transmission power of all P-D2D devices and the BSs fixed, therefore after simplifying the
According to [
Considering the noise is negligible when comparing with
the interference from the P-D2D devices with normalised power is obtained as follows
One of the key objectives of this article is to maximise the cache-throughput probability of the beginning-segments. Based on our analysis the problem can be formulated as
where,
To solve the non-convex optimisation problem
by partial derivative
by partial derivative
which shows that the objective function of problem
In this section, the performance of the S-D2D caching system evaluated using Monte-Carlo simulations. We first discuss the propagation environment and the channel model. Then simulation results are presented. The values of parameters we use in the simulation experiments are summarised in
Parameters | Values |
---|---|
Building dimension | 100 m2 |
Street width | 20 m2 |
5 m | |
200 | |
UE Height | 1.5 m |
100 m | |
B (D2D/Cellular transmission) | 20 MHz |
2.1 GHz | |
2.45 GHz | |
20 dBm | |
43 dBm | |
12 dBm | |
12 dBm | |
0 | |
A1, A2, A3 | 37.8, 36.5, 23 |
Floors | 4 |
5 |
light wall loss parameter for |
[ −20 20] | |
6 | |
4.2 | |
log-normal distribution ( |
We consider a typical and isolated urban-macro cell of dimension (
The propagation channel between a pair of D2D devices is not as same as it is between the BS and the UE. The height of the antenna and the transmission power of the UE is very low as compared to the eNodeB, which limits the area coverage of the D2D communication. Therefore, we cannot directly use the channel models designed for the cellular system for the D2D communication system [
where
where
where
For video streaming, we assume that each device can store 30–90 min (1800 seconds to 5400 seconds) long 1080 p YouTube video. We use the default recommended setting to measure the size of the segments and the video.
According to the default settings, the maximum video file size is 5.4 Gigabytes (GB)
In the simulations first: (i) We distribute the devices randomly in a cell area then, (ii) We assign the beginning-segments of the desired video files in a Block-1 and the subsequent segments in a Block-2 according to the Algorithm 1 and Algorithm 2 respectively. (iii) We generated 200 requests for beginning-segments, and subsequent segments for the large and small videos according to the specified values of
We compare the proposed two-tiered caching approach with two traditional full video caching schemes: the most popular content only (MPCO) caching scheme [
Next, first, in
It is observed from the figures that the average cache-hit ratio, average self-hit ratio and average cache-throughput ratio increases as the size of the beginning-segments decreases. For instance, we can observe that, when each mobile device is caching 10 seconds beginning video clip, we obtained the average cache-hit ratio 29.9–60.3%, average self-hit ratio 10–43% and, average cache-throughput ratio 30.1–62.4% for different values of
Consequently, the requesting users are more likely to find the beginning-segments of the desired video files in their local cache as well as through the D2D link. This result proves the effectiveness of delivering beginning-segments through our BSCP. We can also observe from the figure, that the average performance ratio of the BSCP increases as we increase the value of
When the performance of SLVCP and SPCP is assessed, we observed that both caching policies follow the same trend when the percentage of cache capacity for the Block-2 is increased. However, for large cache space, SLVCP policy has a higher cache performance ratio. For instance, the users of short length videos can not only start the video with zero startup-time, but up to 47.5% of users can enjoy continuous streaming delivery with zero playback-delay. While the 31% of users can download the remaining segments of the desired short length videos from their neighboring devices with low playback-delay. We can also observe that the performance of the SPCP is better than the MPCO strategy. The reason behind that is that SPCP is capable of caching more dynamic and large number of content (fully and partially) in comparison to the MPCO strategy, which concentrates only on highly skewed popular content
In
In this paper, we proposed a two-tiered S-D2D caching approach by taking into consideration one of the important features of on-demand video streaming applications, namely, User Abandonment Behavior. Which is when users stop watching videos before their completion and after watching only a few video chunks of the video. The S-D2D approach divides the cache space of each D2D device into two blocks of different sizes. The first small block of the user’s cache is reserved for storing and delivering only the beginning portion. The second block caches the latter portion of the requested video file’ fully or partially’ depending on the users’ video abandonment behaviour and popularity of the video. We also proposed a segment access control protocol, describing how the video segments are cached and shared in an S-D2D caching system. To control the admission of segments into the user’s cache and improve the system throughput, we further proposed and evaluated three caching algorithms, i.e., BSCP, SPCP and SLVCP. Our simulation results showed that the BSCP achieved the average throughput-ratio from 83.9%–93.6%, among which 79% of users can start the video with zero startup-time through the self-hit, while 13% of requests can be satisfied through the cache-hit. Our simulation results also proved that the SLVCP outperforms all caching policies. We also proved in our simulations that, the SPCP performs better than the MPCO and OCP, even if the caching conditions are not favourable. Our simulation results also proved that the SLVCP outperforms all caching policies.
The Corresponding author K.R would like to express his gratitude to Manchester University for support. The author A. A is thankful to Prince Sattam Bin Abduaziz University for their support.
The cache space of mobile devices in literature is handled as a rich resource. However, they possess heterogeneous and limited storage resources [
The central database keeps the identification information of each P-D2D device such as device-ID, video-ID and its geographic position. The device-ID is a sequence of unique bits that uniquely identify the devices. The video-ID is a string of bits that uniquely identify the video. The geographic position of the mobile device can be discovered by the Global Positioning System (GPS).
This transmission rang has been justified in literature suitable for the device discovery [
The average storage capacity of smartphones varies from 32 GB to 64 GB. Therefore, we assume that each UE in our simulation is capable of caching video files equivalent to one-hour long 1080p YouTube video.
In our simulations, we make sure that each mobile device is caching no duplicates of the video content.