- Open Access
Joint source-channel coding and optimization for mobile video streaming in heterogeneous wireless networks
© Wu et al.; licensee Springer. 2013
- Received: 3 September 2013
- Accepted: 3 December 2013
- Published: 11 December 2013
This paper investigates mobile video delivery in a heterogeneous wireless network from a video server to a multi-homed client. Joint source-channel coding (JSCC) has proven to be an effective solution for video transmission over bandwidth-limited, error-prone wireless networks. However, one major problem with the existing JSCC approaches is that they consider the network between the server and the client as a single transport link. The situation becomes more complicated in the context of multiple available links because involving a low-bandwidth, highly lossy, or long-delay wireless network in the transmission will only degrade the video quality. To address the critical problem, we propose a novel flow rate allocation-based JSCC (FRA-JSCC) approach that includes three key phases: (1) forward error correction redundancy estimation under loss requirement, (2) source rate adaption under delay constraint, and (3) dynamic rate allocation to minimize end-to-end video distortion. We present a mathematical formulation of JSCC to optimize video quality over multiple wireless channels and provide comprehensive analysis for channel distortion. We evaluate the performance of FRA-JSCC through emulations in Exata and compare it with the existing schemes. Experimental results show that FRA-JSCC outperforms the competing models in improving the video peak signal-to-noise ratio as well as in reducing the end-to-end delay.
- Mobile video streaming
- Heterogeneous wireless networks
- Joint source-channel coding
- Flow rate allocation
In the past few years, mobile video streaming (e.g., Youtube  and Hulu ) has become one of the most popular applications, and video traffic headed for handheld devices (e.g., smart cell phones and iPad) has experienced explosive growth. According to the Cisco Visual Index  report, video streaming accounts for 57% of mobile data usage in 2012 and will reach 69% by the year 2017. Global mobile data is expected to increase 13-fold between 2012 and 2017. Furthermore, high-definition video has surpassed the standard definition video by the end of 2012 and will comprise 79% of video traffic by 2016.
Although the proliferation of wireless infrastructures has offered the users with many access options (e.g., cellular networks, wireless local area network (WLAN), and Worldwide Interoperability for Microwave Access (WiMAX)), it is still a challenging problem to efficiently provide mobile video streaming services due to performance limitations of single wireless networks. Current WLAN systems cannot provide satisfactory quality of video streaming services due to the small coverage and relatively limited bandwidth as the number of mobile users increases [4, 5]. Even worse, WLAN systems are not robust enough to sustain user mobility [6, 7]. On the other hand, cellular networks, e.g., Universal Mobile Telecommunications System (UMTS) and HSDPA, can provide more robust wireless connections to mobile users. However, their bandwidth is not adequate to support high-quality video streaming with stringent bandwidth requirements . Although 4G LTE and WiMAX can provide a much higher peak data rate and extended coverage, they are not widely deployed yet and the bandwidth limitation will still become a problem as the wireless spectrum is shared by many users . The performance limitations of single wireless networks naturally turn research attentions to aggregate the bandwidth of heterogeneous wireless networks, and it has already attracted considerable research attentions [8–10]. Conventionally, these bandwidth aggregation algorithms are designed for dynamically allocating video flows with seldom considerations in inherent channel errors and fluctuations, which can significantly impact on the streaming video quality [6, 11].
Motivated by optimizing the JSCC for mobile video delivery in heterogeneous wireless networks, we propose a flow rate allocation-based JSCC (FRA-JSCC) approach in this work. By the term ‘flow rate allocation’, we mean dynamically picking the appropriate wireless access networks and assigning the transmission rates to each of them. First, the video source rate adaption scheme is designed to satisfy the delay requirements of real-time video applications. Second, forward error correction (FEC) redundancy estimation is performed to meet the tolerable loss rate. Third, a simple but effective search algorithm for flow rate allocation is proposed to minimize end-to-end video distortion. Specifically, the contributions of this paper can be summarized in the following:
An efficient end-to-end video delivery scheme in integrated heterogeneous wireless networks that uses JSCC in conjunction with flow rate allocation in order to improve the perceived video quality.
A mathematical model of JSCC to minimize the end-to-end video distortion over multiple wireless channels. The channel distortion is comprehensively analyzed with both transmission and overdue loss.
Extensive semi-physical emulations in Exata with the real-time H.264 video streaming. Experimental results show that (1) FRA-JSCC improves the average video peak signal-to-noise ratio (PSNR) by up to 3.5, 8.45, and 11 dB compared to the fountain code-based virtual path (FCVP) , joint multimedia-FEC rate (JMFR) , and dynamic multi-path (DMP) ; (2) FRA-JSCC reduces the average end-to-end delay by up to 20.8, 11.5, and 40.3 ms compared to the FCVP, JMFR, and DMP; (3) FRA-JSCC mitigates the effective loss rate by up to 6.05%, 10.5%, and 15.5% compared to the FCVP, JMFR, and DMP.
Basic notations used in this paper
Set of wireless access networks, r th access network
Number of available wireless networks
Available bandwidth of P r
Average loss rate of P r
Propagation delay of P r
Effective loss rate
Average burst length of P r
Number of source packets in a FEC block
Total number of FEC packets in a FEC block
FEC code rate
Effective loss rate
Video source (encoding) rate
Selection vector for wireless access networks, r th element
Transmission rate assignment vector, r th element
FEC data packet size
Number of video frames in a GoP
Video frame rate
Maximum delay constraint for each group of pictures
Loss requirement imposed by the video application
The related work to this paper can be generally categorized into two branches: joint source-channel coding and video delivery in heterogeneous wireless networks. We will discuss on each topic respectively in this section.
2.1 Joint source-channel coding
In summary, the JSCC problem includes joint coding and optimal rate calculation for video coding and channel coding, which provides various protection level to the video data according to its level of importance and channel conditions. Most of the related work in video transmission focus on (1) finding an optimal bit rate for video coding and channel coding, e.g., [19, 20]; (2) designing the video coding mechanism to achieve the target source rate under given channel conditions, e.g., ; (3) designing the channel coding to achieve the required reliability, e.g., low-density parity check , turbo , Reed-Solomon (RS) , and fountain  codes; (4) designing joint optimization framework, including all available error control components together with error concealment and transmission control, to improve global system performance, e.g., . The authors of  deal with the optimal allocation of MPEG-2 encoding and media-independent forward error correction rates under the total given bandwidth. They define optimality in terms of minimum perceptual distortion given a set of video and network parameters. They compute the network error parameters after FEC decoding and derive the global set of equations that lead to optimal dynamic rate allocation. In a more recent work , Ji et al. studied on the optimization approach of JSCC for layered video broadcasting to heterogeneous devices. The objective is to achieve maximum overall receiving quality of the heterogeneous quality of service (QoS) receivers.
All these works consider the network as a single transport link between the server and the client. They do not address multi-path streaming scenarios, where more than one network path is allocated to the application. Different from previous JSCC approaches, Jurca et al.  researched on the optimal FEC scheme and layer selection in multi-path scenario. This approach uses a multi-layer coded video stream, and the base-layer stream is protected by duplicated transmission using multiple physical paths during the handoff. However, the major flaw is that it is generally under the assumption that all the wireless networks are reliable for improving the overall video quality and thus lacks effective network selection algorithm.
2.2 Video delivery in heterogeneous wireless networks
Video delivery in heterogeneous wireless networks has recently attracted much attention, and the general review can be referred to [27, 28]. In the Earliest Delivery Path First  algorithm, it takes into account the available bandwidth, propagation delay, and video frame size to estimate the arrival time and aims to find an earliest path to deliver the video packet. The load balancing algorithm (LBA)  performs stream adaption in response to varying network status by only transmitting those packets which are estimated to arrive at the client within the decoding deadline and conserves bandwidth by dropping packets that cannot be decoded because they rely on previous packets that have been dropped. A packet prioritization scheme in LBA gives a higher weight to I frames over B and P frames and also to base layer packets over enhancement layer packets. The LBA scheduler sorts packets according to priority weighting and sacrifices lower priority packets to ensure the delivery of those with a higher priority. Song et al.  propose a probabilistic multi-path transmission (PMT) scheme, which sends video traffic bursts over multiple available channels based on a probability generation function of packet delay. PMT is not robust to client mobility as it does not dynamically adjust the flow splitting probability according to time-varying channel status. Han et al.  proposed an end-to-end virtual path construction system over heterogeneous wireless networks based on fountain code. The goal of this system is to maximize the encoding bit rate on the basis of aggregate bandwidth as well as overcoming the channel loss. However, the big block size of fountain code will lead to a long delay, which is not appropriate for real-time video streaming over the bandwidth-limited and time-varying wireless networks.
Besides, encoded multi-path streaming (EMS)  and multi-path loss tolerant (MPLOT)  are typical protocols exploiting path diversity in wired/wireless multi-path networks based on erasure code. EMS scheme splits traffic loads over multiple paths according to the path loss rate and dynamically adjusts FEC redundancy. However, EMS was generally under the assumption that all the available paths could be beneficial for the transmission as in . MPLOT is a transport protocol that aims at maximizing the throughput of the upper layer application. However, MPLOT cannot guarantee real-time video delivery as it does not address tight delay constraints.
3.1 Network model
The end-to-end connection from the video server to the wireless interface of the mobile client is considered as an independent physical path which includes the wired and wireless domains. It is well known that the wireless access is most likely to be the bottleneck link for the end-to-end transmission due to the limited bandwidth and time-varying channel status. The transmission data packets may encounter loss due to buffer overflow in immediate routers or erasures caused by channel fading in the error-prone wireless channels. In order to simplify the discussion, we generally consider a packet to be lost due to the link fault either in the wired/wireless packet switching networks. Each physical path P r is associated with the following metrics:
Available bandwidth μ r (expressed in Kbps). μ r captures the variation of background traffic and bandwidth fluctuation.
Propagation delay t r which includes the link delays of the wired and wireless networks.
Average loss probability , assumed to be an i.i.d process and independent of the video streaming rate.
We model the burst loss behavior on each physical path by the continuous-time Gilbert model. It is a two-state stationary continuous time Markov chain. The state assumes one of two values: G (good) or B (bad). If a packet is sent at time t with , then the packet can be successfully delivered. Otherwise, when , then the packet is lost.
The available bandwidth and propagation delay of each wireless network can be estimated by packet probing mechanisms (e.g., the pathChirp  algorithm employed in this work) over each interface of the mobile client. The loss parameters and can be sensed through control protocols or delay measurements .
3.2 Video distortion model
in which α, V0, D0, and β are constants for a specific video codec and video sequence. These parameters can be estimated from three or more trial encodings using nonlinear regression techniques. To allow fast adaptation of the flow rate allocation to abrupt changes in the video content, these parameters can be updated for each group of pictures (GOP) in the encoded video sequence . Since this model takes into account the effects of intra-coding and spatial loop filtering, it provides accurate estimates for end-to-end distortion .
3.3 Forward error correction
In this work, we use the systematic RS block erasure code for video data protection against channel losses. Generically, a FEC block of n data packets contains k source packets and n-k redundant packets. Usually, the receiver can fully reconstruct the original k data packets if at least k packets of the FEC block are correctly received. In FEC (n,k) code, for every k source packets, (n-k) redundant data packets are introduced to make up a codeword of packets. As long as a client receives at least k out of the n data packets, it can recover all the source packets. If the number of received packet is less than k, the arrival source packets can still be used to contribute to the video decoding process because they have been kept intact by the RS encoding process. In general, for the same code rate k/n, increasing the value of n would enhance the performance of RS code. The FEC code rate n/k needs to be dynamically chosen based on the loss requirement and channel status.
3.4 Effective loss rate
For real-time video applications, each video frame is associated with a decoding deadline. This deadline sets a maximum delay bound for a frame to be successfully delivered to the client in order to contribute to the decoding process. Next, we will provide a comprehensive analysis for the transmission and overdue loss, respectively.
3.4.1 Transmission loss rate
in which denotes that any b out of a consecutive packets are lost given that this block is preceded by a packet which is state q. The detailed computations of can be referred to .
3.4.2 Overdue loss rate
3.5 Problem formulation
This is a nonlinear optimization problem with linear constraints. With regard to the computational cost and convergence, it is impractical to derive the exact solution for the minimal video distortion. In the next section, we will show how to resolve this optimization problem in the design of the proposed FRA-JSCC.
At the client side, the video frames will be stored in the playback buffer after the FEC decoding process. The inter-frame resequence step aims at reordering the video frames in case they arrive at the client out-of-order. As each video frame is associated with a decoding deadline, the overdue frames will be discarded and concealed by copying from the last received ones. Next, we will describe the key components in the system design and their working steps.
4.1 FEC redundancy estimation
For estimating the FEC redundancy, we model the multiple wireless networks as a single virtual link with effective loss rate . Consider the transmission of k FEC packets (each of size S) over the virtual link from the source to the destination. Let (n - k)/k denote the redundancy (i.e., the fraction of redundant FEC packets in the FEC block). There is an inherent tradeoff between FEC redundancy and its error correction power . With more redundant packets, the receiver can recover from more severe losses, at the cost of larger end-to-end delays and higher loads imposed on networks. Therefore, in the design of FRA-JSCC, the goal is to use ‘just enough’ FEC redundancy to meet the video application’s loss requirement (Δ). With this objective, the FEC adaption policy can be derived under fairly general assumptions by simply bounding the loss tail probability.
and can be estimated using Equation 12. Therefore, the FEC redundancy can be obtained i.f.f Φ is determined.
4.2 Source rate adaption
According to the information theory , video source distortion can be minimized by increasing the effective encoding rate. On the other hand, the increasing encoding rate will lead to higher transmission rate which imposes heavier load on channels. If the imposed load exceeds the network capacity, it will in turn result in longer delay and packet loss due to network congestion. There is an inherent conflict between the source and channel distortion. Therefore, the critical point in source rate adaption is to find the upper bound under application and channel constraints. The constraint imposed by video applications is the delay requirements. In real-time video applications, delay plays a vital role in enhancing streaming video quality. If a video frame arrives at a destination past the decoding deadline, it is considered lost. In this paper, we propose a source rate adaption algorithm under delay requirements, taking into account FEC redundancy carried out in the last subsection.
4.3 Flow rate allocation
Practically, a mobile device has a small number of network interfaces due to the limited battery life, mobility, cost, etc. Thus, the computational complexity required for the proposed flow rate allocation algorithm is negligible although the full search method is used.
4.4 Channel status monitoring
A client periodically reports information on each physical path to a parameter control unit of a server through the most reliable uplink channel. This information is used to determine the results of FEC parameter tuning, source rate adaption, and source rate allocation in the system design. The procedures of the proposed FRA-JSCC are presented in Algorithm 1.
Algorithm 1 Flow Rate Allocation based Joint Source Channel Coding.
In this section, we evaluate the efficacy of the proposed FRA-JSCC by comparing it with the existing schemes for video delivery over heterogeneous wireless networks. We first describe the emulation methodology that includes the emulation setup, reference schemes, performance metrics, and emulation scenario.
5.1 Emulation methodology
5.1.1 Emulation setup
Exata 2.1  is used as the network emulator. Exata is an advanced edition of QualNet  in which we can perform semi-physical emulations. In order to implement the real video streaming-based emulations, we integrate the source code of JSVMa (as Objective File Library (.LIB)) with Exata and develop an application layer protocol of ‘Video Transmission’. The detailed descriptions of the development steps could be referred to Exata Programmer’s Guide . In the emulation topology, the video server has one wired network interface and the mobile client has three wireless network interfaces, i.e., cellular, WLAN, and WiMAX. We can construct an end-to-end connection to a specific wireless network interface by binding a pair of IP addresses from the server and the client. The configurations of the emulated background traffic in the wired networks are listed in Table 2. The server and client are mapped to real computers, which are connected to the emulation server through the Exata Connection Manager. The IEEE 802.11b is adopted as the WLAN protocol. The configurations of heterogeneous wireless networks are summarized in Table 3[4, 5, 45].
Parameters of background traffic
Start time (s)
Packet size (bytes)
Traffic rate (Mbps)
Rand [10,1500] KB
Rand [5,50] MB
Parameter configuration of wireless networks
Target SIR value
Common control channel power
Maximum power of BS
Total cell bandwidth
Inter/intra-cell interference ratio
Background noise power
Average loss rate
Average burst length
Number of carriers
Average loss rate
Average burst length
Average channel bit rate
10 μ s
Maximum contention window
Average loss rate
Average burst length
H.264/SVC reference software JSVM 9.18  is adopted as the video encoder. The generated video streaming is encoded at 30 frames per second and a GOP consists of 8 frames. The test video sequences are Foreman, Mother & Daughter, Hall, and Container in QCIF (quarter common interchange format) with 300 frames. Each of the sequences features a different pattern of temporal motion and spatial characteristics which is reflected in their corresponding video quality versus encoding rate dependencies. We concatenate them 10 times to be 3,000 frames long in order to obtain statistically meaningful results. The loss requirement (Δ) and delay constraint ( ) are set to 1% and 250 ms, respectively.
5.1.2 Reference schemes
We compare the performance of FRA-JSCC with the following schemes for video delivery in heterogeneous wireless networks:
FCVP []. As the system proposed in  aims at exploiting the path diversity in heterogeneous wireless networks based on fountain code, we name it fountain code-based virtual path construction system. In the implementation of FCVP, the control parameters were updated for every 0.5 s. The symbol and packet size is set to be 8 and 512 bytes, respectively.
JMFR []. The joint multimedia-FEC rate allocation scheme computes the optimal source and FEC rate for scalable video over multi-path networks based on the utility algorithm. The number of video layers is set to be 1 in all the emulations.
DMP []. The dynamic multi-path streaming utilizes multiple paths by maintaining a transmission control protocol (TCP) connection on each path. The sender puts the data packets in a single sender queue. At any time, only one TCP connection can gain the access to the sender queue. The winning TCP connection will keep sending data until the connection is blocked. Another available TCP connection will then gain the access to the sender queue and continue sending data. In order to fairly compare the performance with other competing models, we dynamically adjust the video encoding rate based on the aggregate bandwidth of the available links.
5.1.3 Performance metrics
We adopt the following performance metrics to evaluate the proposed approach against the above competing approaches:
PSNR. Peak signal-to-noise ratio is a standard metric of video quality and is a function of the mean square error between the original and the received video frames. If a video frame is lost or past the deadline, it is considered lost but may be concealed by copying from the last received frame before it.
Average end-to-end delay. The end-to-end delay of a video frame consists of delay in the network and the resequencing time at the client. It is counted from the generation time of a video frame to the time when it can be decoded.
Effective loss rate. As introduced in Section 3.4, the effective loss rate includes the transmission and overdue loss. PSNR measures video quality after error concealment for the lost video frames. We measure the effective loss rate to testify the competing models in mitigating the packet loss.
5.1.4 Emulation scenario
We conduct all the emulations in the mobile scenario with trajectories indexed from 1 to 4 as shown in Figure 5. The four mobile trajectories represent the different access options for the mobile user in the integrated heterogeneous wireless networks, e.g., the user could simultaneously access the UMTS and WiMAX while moving along the first trajectory. The mobile client requests to the server through a wireless interface and constructs the connection whenever it moves in the coverage. The moving speed of the client is set to be 2 m/s in all the emulations. In all the emulations, the components of FRA-JSCC are working at the GOP level, i.e., every 0.25 s. It is necessary to update the JSCC parameters for each GOP due to the time-varying wireless channel status. However, with regard to the coding efficiency, it is impractical to trace the rate variation at the video frame level.
For the confidence results, we repeat each set of emulations with different video sequences more than five times and obtained the average results with a 95% confidence interval. The microscopic and mobility results were presented with the measurements of finer granularity.
5.2 Evaluation results
Average PSNR values for different compared models
Mother & daughter
5.2.2 Average end-to-end delay
5.2.3 Effective loss rate
In this paper, we have presented a flow rate allocation-based JSCC approach for mobile video delivery in heterogeneous wireless networks. Through modeling and analysis, we have developed solutions for FEC redundancy adaption, video source rate adaption, and flow rate allocation. Experimental results show that the proposed FRA-JSCC is able to dynamically select the appropriate wireless access networks out of all candidates and significantly improve the video PSNR. As future work, we will consider (1) designing a seamless vertical handoff algorithm for optimal-quality video in the integrated WLAN, WiMAX, and cellular networks. The work in  formulates the heterogeneous wireless networks as restless bandit systems. However, it does not provide in-depth analysis on the physical characteristics (e.g., the coverage and received signal strength) of each wireless network. We would also consider (2) including an optimal path interleaving mechanism with the FRA-JSCC to overcome the burst loss.
a We choose the JSVM in convenience for the source code integration as both Exata and JSVM are developed using the C++ code, while the H.264/AVC JM (http://iphome.hhi.de/suehring/tml/) software is developed using C language.
This research is supported by the National Grand Fundamental Research 973 Program of China under grant nos. 2011CB302506, 2012CB315802, and 2013CB329102; Research Program of Chongqing Municipal Education Commission (grant no. KJ130523); CQUPT Research Fund for Young Scholars (grant no. A2012-79); National Key Technology Research and Development Program of China ‘Research on the mobile community cultural service aggregation supporting technology’ (grant no. 2012BAH94F02); Novel Mobile Service Control Network Architecture and Key Technologies (2010ZX03004-001-01); National High-tech R &D Program of China (863 Program) under grant no. 2013AA102301; National Natural Science Foundation of China under grant nos. 61003067, 61171102, 61001118, and 61132001; Program for New Century Excellent Talents in University (grant no. NCET-11-0592); Project of New Generation Broadband Wireless Network under grant no. 2011ZX03002-002-01; and Beijing Nova Program under grant no. 2008B50. The authors would like to express their gratitude to the anonymous reviewers who provided comments to improve the paper quality.
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